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Test cases play crucial role in software testing. The exhaustive testing of large complex object oriented software systems has been found to be impractical due to large number of test cases execution cost. Due to this reason the researchers in the field of software testing reduce the number of test cases by selecting only effective and important test cases. This paper presents an approach for test case reduction for object oriented systems considering the security as main aspect of software system. Finding out less secure classes help software testers to remove redundant test cases at class level. Object oriented class level security metrics have been considered to detect less secure classes. To evaluate proposed approach, jEdit 5.5.0 software as a case study has been considered. Weka 3.8 was used to generate the proposed mathematical model in order to select effective metrics to detect all the less secure classes. Using feature selection techniques in Weka, effective and ineffective security metrics were categorized. Only effective metrics are taken into account for assigning weighs to each test paths. The results showed the significant improvement in results.

jEdit5.5.0, Test Case, Test Cases Reduction

[1] M. Harrold, R. Gupta and M. Soffa, “A methodology for controlling the size of a test suite,” ACM Transactions in Software Engineering and Methodology, Vol. 2, No. 3, 1993, pp. 270-285.
[2] A. Agrawal, S. Chandra, and R.A. Khan, “An Efficient Measurement of Object-Oriented Design Vulnerability”, In Proceedings of International Conference on availability, Reliability and Security, Fukuoka, Japan, 1619 March 2008, ARES 2009
[3] Zhang Guangquan and Rong Mei, “An Approach Of Concurrent Object-Oriented Program Slicing Based On LTL Property”, International Conference on Computer Science and Software Engineering, pp. 650-653,2008
[4] S. Chandra and R. A. Khan, “Software Security Metric Identification Framework” International Conference on Advances in Computing, Communication and Control, pp. 725–731, 2009
[5] B. Alshammari, C. Fidge, and D. Corney (2009), “Security Metrics for Object-Oriented Class Designs,” 9th International Conference on Quality Software, Jeju, pp. 11-20,2009.
[6] B. Alshammari, C. Fidge, and D. Corney (2010), “Security Metrics for Object-Oriented Designs,” 21st Australian Software Engineering Conference, pp 55-64, 2010.
[7] D. M. Thakore and S. J. Sarde, “Assessing the Software Complexity and Security metrics from UML Class diagram,” International Journal of Engineering Research and Applications, vol. 2, no. 4, pp. 585–587, 2012.
[8] S. H. Gandhi, D. R. Anekar, M. A. Shaikh, and A. A. Salunkhe, “Security Metric for Object Oriented Class Design- Result Analysis,” International Journal of Innovative Technology and Exploring Engineering, vol no. 6, pp. 139–144, 2013.
[9] N. Frechette, L. Badri, and M. Badri, “Regression Test Reduction for Object-Oriented Software : A Control Call Graph Based Technique and Associated Tool,” Hindawi Publishing Corporation ISRN Software Engineering, 10 pages, vol. 2013.
[10] S. H. Gandhi, D. R. Anekar, M. A. Shaikh, and A. A. Salunkhe, “Finding Accessibility and Interaction Vulnerability of Rational Rose Class Design Using Design Metrics,” International Journal Of Scientific and Engineering Research, vol. 4, pp. 1–5, 2013.
[11] Devendra Singh Thakore and Dr. Akhilesh R Upadhyay, “A System for Identification and Assessment of Secure Design using Dynamic Security Metrics,” Journal Of Information, Knowledge and Research in Computer Engineering, vol. 2, pp. 276–278, 2013
[12] Vedpal, N. Chauhan, “Regression Test Selection for Object Oriented Systems Using OPDG and Slicing Technique,” 2nd International Conference on Computing for Sustainable Global Development, pp.1371-1378, 2015
[13] S. K. Mohapatra and S. Prasad, “Test Case Reduction Using Ant Colony Optimization for Object Oriented Program,” International Journal Of Electrical and Computer Engineering, vol. 5, no. 6, pp. 1424–1432, 2015.
[14] S. K. Mohapatra and M. Pradhan, “Finding representative test suit for test case reduction in regression testing,” International Conference on Computer, Communication and Control, Indore, 2015, pp. 1-6.
[15] S. A. Khan and R. A. Khan, “Security Improvement of Object Oriented Design using Refactoring Rules,” International Journal of Modern Education and Computer Science, vol. 2, pp. 24–31, 2015.
[16] B. M. Alshammari, “A Generic Model for Assessing Multilevel Security-Critical Object-Oriented Programs,” International Journal Of Advanced Computer Science and Applications, vol. 7, no. 11, pp. 419-427, 2016.
[17] B. Geetha and D. Jeya Mala, (2016) “Automatic Test Case Reduction in Object Oriented System Using Clustering and Fuzzy Logic,” Asian Journal of Information Technology, Vol. 15, no. 20, pp. 4071-4076.
[18] A. Al Hussein, “An Object-Oriented Software Metric Tool to Evaluate the Quality of Open Source Software,” International Journal Of Computer Science And Network Technology, vol. 17, no. 4, pp. 345–351, 2017.
[19] P. Bhandari, “Review of Object-Oriented Coupling Based Test Case Selection In Model Based Testing,” International Conference on Intelligent Computing and Control Systems, pp. 1161–1165, 2017.
[20] A. Marchetto, G. Scanniello, and A. Susi, “Combining Code and Requirements Coverage with Execution Cost for Test Suite Reduction,” IEEE Transactions on Software Engineering, vol. 5589, no. c, pp. 1–28, 2017.
[21] S. Dwivedi, “Minimization of Test Suites for Fuzzy Object-Oriented Database,” International Journal Of Computer Applications, vol.179, no. 43, pp. 10–15, 2018.

Sameeksha Khare, Rajvir Singh, Ajmer Singh, "Test Case Reduction for Object Oriented Systems using Security Metrics," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.371-378, 2019.

The Bombay Stock Exchange (BSE) is India’s premier and most prestigious stock market. A stock market is a facilitation centre for trading (buying and selling) of stocks of various companies. Its index is calculated as a combination of stock prices of several companies enlisted in the exchange. The stock market is characterized by its endless and unpredictable troughs and crests. This paper attempts to analyse the BSE Sensex Closing Index data over a span of the last decade collected on the last day of the month (from May, 2009 to April, 2019). It also attempts to predict the BSE Sensex closing data for a future span of 10 years at a monthly frequency. The paper also does a accuracy testing of the predictive model generated. This work would be beneficial for both the nation and a trading individual. The Stock market indices reflect the health of a nation’s economy and its direction and growth. A trading individual would benefit in his pursuit of profit making by taking correct investment decisions based on accurate predictions made. The paper uses the ARIMA model for timeseries analysis and for generating a predictive model for making future forecasts.

Arima model, Sensex forecasting, Short-term prediction, Stock market prediction, Time Series analysis

[1] D. Banerjee, "Forecasting of Indian stock market using time-series ARIMA model", In the proceedings of 2nd IEEE International Conference on Business and Information Management (ICBIM), pp. 131-135 , January 2014.
[2] Ayodele A. Adebiyi, Aderemi O. Adewumi, Charles K. Ayo , "Stock price prediction using the ARIMA model" , In proceedings of the 16th IEEE International Conference on Computer Modelling and Simulation (UKSim) , pp. 106 -112 , March 2014 .
[3] A. Edward, J. Manoj, “Forecast model using arima for stock prices of automobile sector” , International Journal of Research in Finance and Marketing , pp. 1-9 , April 2016.
[4] Mohamed Ashik. A, Senthamarai Kannan. K , “Forecasting national stock price using arima model”, Global and Stochastic Analysis , pp. 71-81, January 2017.
[5] M. Angadi, A. Kulkarni , “Time Series Data Analysis for Stock Market Prediction using Data Mining Techniques with R”, International Journal of Advanced Research in Computer Science , Pp. 104-108, August 2015.
[6] P. Mondal, L.Shit , S.Goswami , “Study of effectiveness of time series modeling (ARIMA) in forecasting stock prices”, International Journal of Computer Science, Engineering and Applications, pp. 13-29, April 2014.
[7] Kamalakannan J , I. Sengupta , S. Chaudhury , “Stock Market Prediction using TimeSeriesAnalysis” , Computing Communications and Data Engineering Series , Volume No. 01, Issue No. 03 , pp. 1-5 , 2018 .
[8] J.V.N. Lakshmi , Ananthi Sheshasaayee, “A Big Data Analytical Approach for Analyzing Temperature Dataset using Machine Learning Techniques” , International Journal of Scientific Research in Computer Sciences and Engineering , Volume No. 05 ,Issue No. 03, pp. 92-97 , June 2017.
[9] Himanshi , Komal Kumar Bhatia, “ Prediction Model for Under-Graduating Student’s Salary Using Data Mining Techniques” , International Journal of Scientific Research in Network Security and Communication, Volume No. 06 ,Issue No. 02, pp. 50-53 , April 2018.

Debaditya Raychaudhuri, "BSE Sensex Closing Index Data Analysis and Forecasting using the ARIMA Model," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.379-389, 2019.

Cloud computing is an information technology (IT) paradigm that enables ubiquitous access to shared pools of configurable system resources and higher-level services that has provisioned with minimal management effort over the Internet. The main enabling technology for cloud computing is Virtualization, which is essentially creating scalable system of multiple independent computing devices. With virtualization, idle computing resources have allocated to user more effectively. Allocation of idle computing resource is one of the major problem faced today in cloud computing. Adopting to right resource allocation algorithms can resolve the problem of resource allocation. Backfilling algorithms are better than the existing First Come First Serve algorithms (FCFS) used for resource allocation. In this paper, various `Backfilling` algorithms are surveyed. Further analysis on the performance of each algorithm in terms of response time, throughput, waiting time, turn-around time, job migration between queues are measured.

Cloud Computing, Virtualization, First Come First Serve, Backfilling, Job Migration

[1] D. Tsafrir, Y. Etsion, and D. G. Feitelson, “Backfilling Using Runtime Predictions Rather than User Estimates,” School of Computer Science and Engineering, Hebrew University of Jerusalem, Tech. Rep. TR 2005-5, 2003. [2] Bhupesh Kumar Dewangan, Amit Agarwal,Venkatadri M, Ashutosh Pasricha,” Resource Scheduling in Cloud: A Comparative Study”, International Journal of Computer Sciences and Engineering, Vol.-6, Issue-8, pp. 168-173,Aug 2018.
[3] Rajnish Choubey et al., “A Survey on Cloud Computing Security, Challenges and Threats”, International Journal of Computer Sciences and Engineering, Vol. 3,No. 3 ,pp.1227-1231,Mar 2011.
[4] D. G. Feitelson, L. Rudolph, and U. Schwiegelshohn, “Parallel job scheduling strategies for parallel processing”, In Proceedings of the 10th International Conference on Job Scheduling Strategies for Parallel Processing, ser. JSSPP’04. Berlin, Heidelberg: Springer-Verlag, pp. 1–16 , 2005.
[5] Dror G. Feitelson and Ahuva Mu’alem Weil., “Utilization and predictability in scheduling the IBM SP2 with backfilling”, In Proceeding of the 12th International Parallel and Distributed Processing Symposium, pp. 542–546, 1998.
[6] U. Schwiegelshohn and R. Yahyapour., “Fairness in Parallel Job Scheduling”, “Journal of Scheduling”, 3(5) pp:297-320. John Wiley, 2000.
[7] Shahabanath K K, Sreekesh Namboodiri T, “K-Tier And Selective Backfilling Approach for Parallel Workload Scheduling in Cloud”, International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), Volume 3 Issue 9, September 2014.
[8] Xiaocheng Liu, Chen Wang, Bing Bing Zhou, Junliang Chen,Ting Yang, and Albert Y. Zomaya, “Priority-Based Consolidation Of Parallel Workloads In The Cloud”, IEEE Transactions On Parallel And Distributed Systems, Vol. 24, No. 9, September 2013.

V. Nisha, S. Vimala, "A Survey of Backfilling Algorithms in Cloud Resource Allocation," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.390-394, 2019.

Fog computing has expanded the horizons of cloud computing services near to the users. Firstly the IoT devices were used exclusively VMs but this results in high energy consumption. At the cloud layer the problem of resource utilization has been practised. But at the Fog layer the problem of Overutilization and Underutilization of resources occur. So, to overcome this problem we are designing a model for resource utilization that achieves best result for both service provider and end users. The current system incorporates an adaptive way for the utilization of resources at the cloud layer. But at the Fog layer the adaptive way of cloud layer are not being practised. The Fog layer allows pre-processing of requests but have limited resources. In order to overcome this problem, this paper, proposes a mechanism for improving the resource utilization at fog computing layer. Our proposed model is not only beneficial for the fog service providers in terms of good resource utilization but also equally beneficial for the fog service users in terms of good response time.

Fog Computing, Internet of things (IoT), Virtual Machine (VM), Quality of Service (QoS), Data Centers (DC)

[1] Xu et al. “Dynamic resource allocation for load balancing in fog environment,” Vol. 2018.
[2] Deyu Qi et al. “A threshold-based dynamic resource allocation scheme for cloud computing,”pp.695-703, 2011.
[3] Vadde Usha , Dr. Vijaya Sri Kompalli ,”survey of resource management techniques in fog computing,” pp. 3761-3765,2018.
[4] P. Hu, S. Dhelim, H. Ning, and T. Qiu, “Survey on fog computing: architecture, key technologies, applications and open issues,” vol. 98, pp. 27–42, 2017.
[5] P.Jue et al. “Fog Computing: Towards Minimizing Delay in the Internet of Things,”2017.
[6] Buyya et al. “latency-aware application module management for fog computing environments,” article 9 November 2018.
[7] Turuk et al. “Mathematical Modeling of QoS-Aware Fog Computing Architecture for IoT Services,” pp.13-21, 2019.
[8] A. Khakimov, A. Muthanna, M. Muthanna,” Study of Fog Computing Structure,” pp.51-54, 2018.
[9] Z. Patrikakis et al. “A Cooperative Fog Approach for Effective Workload Balancing,”pp.36-45, 2017.
[10] O. Salman, I. Elhajj, A. Kayssi and A. Chehab “Edge computing enabling the Internet of Things,” pp. 603-608, 2015.
[11] M. Aazam and E. N. Huh, “Dynamic resource provisioning through Fog micro datacenter,” 2015.
[12] C. Prazeres, M. Serrano, “SOFT-IoT: Self-Organizing FOG of Things,” 2016.
[13] Hua-Jun Hong,” From Cloud Computing to Fog Computing: Unleash the Power of Edge and End Devices,” pp.331-334, 2017.
[14] Yung-Chiao Chen, Yao-Chung Chang,” Cloud-Fog Computing for Information-Centric Internet-of-Things Applications,”pp.637-640, 2017.
[15] Samson Busuyi Akintoye,Antoine Bagula,” Improving Quality-of-Service in Cloud/FogComputing through Efficient Resource Allocation ,”2019.
[16] Cheol-Ho Hong, Blesson Varghese,” Resource Management in Fog/Edge Computing: A Survey,”2018.
[17] Deeksha Arya, Mayank Dave,”Security-Based Service Broker Policy for Fog Computing Environment,”2017.
[18] R. Buyya et al. “Fog computing: Principles, architectures, and applications,” 2016.
[19] J. Kumar, A. Malik, SK. D hurandher ,” Demand-Based Computation Offloading Framework for Mobile Devices,”2017.

Himani Nassa, Jitender Kumar, "An Improved Resource Utilization System for Fog Computing Environment," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.395-400, 2019.

Information Analysis and extraction is difficult due to huge amount of data on the Internet. Recommender Systems provide efficient and useful information for user according to their preferences. Large numbers of research have been accomplished on Emotion based Recommender systems Techniques. These techniques extract the human emotions for any items from reviews. In this paper we summarize the existing techniques to extract emotions from reviews written by users for different items and propose a new method to design a dynamic search engine which will extract the online reviews and recommend items of different category on the basis of user search. Further our proposed technique will recommend items to user by combination of online reviews and ratings of product too. The spam reviews will be identified and removed.

Recommender system, emotions, collaborative, content based reviews

[1]F.O.IsinkayeY.O.FolajimiandB.A.Ojokoh“Recommendation systems: Principles, methods and evaluation”,Egyptian Informatics Journal,Volume 16, Issue 3, November 2015, pp: 261-273
[2] Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl “Item-Based Collaborative Filtering Recommendation Algorithms”Proceedings:10th international conference on World Wide Web, Pages 285-295 Hong Kong, Hong Kong — May 01 - 05, 2001 ACM New York, NY.
[3] Micheal j.Pazzami and Daniel Bilsus “Content based Recommendation System”Book The adaptive web Springer-Verleg Berlin, Heidlbergpp: 325-341. ISBN:978-3-540-72078-2,2007.
[4] Simon Philio and P.B Shola “A Paper Reccomender System Based on the Past Rating of the User”International journal of Advanced Computer Technology, Volume 3,Issue 6,pp 40-46, Dec 2015.
[5] Dhoha Almazro, Ghadeer Shahatah, Lamia Albdulkarim, Mona Kherees, Romy Martinez and William Nzoukou “A Survey Paper on Recommender Systems”Published 2010 in ArXiv.
[6] R. W. Picard, “Affective computing: challenges” International Journal of Human-Computer Studies, Vol. 59, No. 1, pp. 55--64, 2003.
[7] Russel JA (1980) A circumplex model of affect. J Pers Soc Psychol 39:1161–1178. doi:10.1037/h0077714
[8] Miller GA (1995) WordNet: a lexical database for English. Commun ACM 38(11):39–41
[9] Kumar A, Kumar N, Hussain M, et al “Semantic clustering-based cross-Domain recommendation” Proc Computational Intelligence and Data Mining (CIDM): pp: 137–141, 2014.
[10] SaraswatM, Chakraverty S,Mahajan N, Tokas N (2016) “On using reviews and comments for cross domain recommendations and decision making”. In: Computing for Sustainable Global Development (INDIACom), 2016 3rd International Conference on IEEE, p 3656–3659
[11] Winoto P and Tang TY“The role of user mood in movie recommendations”Expert Systems with Applications,Elsevier Volume 37, Issue 8, August 2010, Pages 6086-6092
[12] Peng Li,Seiji Yamada “A Movie Recommender System based on Inductive Learning” IEEE Conference on Cybernetics and Intelligent Systems Singapore, 1-3 December, 2004.
[13] Shi Y, Larson M and Hanjalic A“Mining mood-specific movie similarity with matrix factorization forcontext-aware recommendation” Proc of the workshop on context-aware movie recommendation, pp: 34–40, 2010.
[14] Peleja, Filipa, et al. "A recommender system for the TV on the web: integrating unrated reviews and movie ratings" Multimedia systems 19.6 (2013): 543-558.
[15] Chen, Guanliang, and Li Chen. "Augmenting service recommender systems by incorporating contextual opinions from user reviews" User Modeling and User-Adapted Interaction 25.3 (2015): 295-329
[16] Sun, Lihua, Junpeng Guo, and Yanlin Zhu. "Applying uncertainty theory into the restaurant recommender system based on sentiment analysis of online Chinese reviews." World Wide Web (2018): 1-18.
[17] Zhijun Yan, Meiming Xing, Dongsong Zhang,Baizhang Ma (2015) “Exprs: An Extended Page rank method for product feature extraction from online consumer reviews” Information & Management 52.7 (2015): 850-858.
[18] Sulthana, A. Razia, and Subburaj Ramasamy. "Ontology and context based recommendation system using Neuro-Fuzzy Classification" Computers & Electrical Engineering (2018).

Veepu Uppal, Rajesh Kumar Singh, "Proposed Model for Emotions Based Recommender Systems Using Reviews," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.401-405, 2019.

Smart agriculture is taking over traditional farming due to the evolution of the IoT (Internet of Things). Automatic irrigation systems as well as monitoring of environmental parameters are the key factor of proposed system. In this project, we have proposed Real time approach for smart agriculture based on IoT and Raspberry pi 3. For monitoring environmental parameters proposed system have used different sensors like soil moisture sensor, thermistor, DHT11 sensor, Gas sensor. These sensors are connected to ESP8266MOD through MCP3204.Proposed systems have used four nodes at four different farm fields which communicates with Raspberry pi 3 over Wi-Fi. Data from all sensors of all nodes is shown on webpage. Also if any sensor value exceeds threshold level, email alert is sent. If the soil moisture sensor of any node exceeds threshold value motor will be automatically OFF. Using past environmental data of all fields farmers can take better decision for future farming like which crop has to be planted at which period and at which field.

Smart agriculture, Internet of Things, soil moisture sensor, environmental parameters

[1] John A. Stankovic, Life Fellow, “Research direction for the Internet of Things” IEEE, 2014.
[2] Kyunf chang lee, Hong hee lee, “Network based fire detection system via controller area network for smart home automation", IEEE, 2014.
[3] Venkatesh Neelapala, Dr. S. Malarvizhi, “Environment monitoring system based on wireless sensor networks using open source hardware”, IEEE, 2015.
[4] Ala Al-Fuqaha, Mohsen Guizani, Mehdi Mohammadi, Mohammed Aledhari, Moussa Ayyash, “Internet of Things: A survey on enabling Technologies, Protocols and Applications", IEEE, 2015.
[5] C.Anton-Haro and M. Dohler, “Machine-to-Machine Communications: Architecture, Performance and Applications,”1st ed., Wood head Publishing Ltd. Jan. 2015.
[6] C. Pielli et al., “Platforms and Protocols for the Internet of Things,” Endorsed Transactions on Internet of Things, vol. 15, no. 1, Oct. 2015.
[7] C.Zhu, V.Leung, L.Shu andE. C. H Ngai, “Green Internet of Things for smart world”IEEE, 2015, vol. 3, pp.2151-2162.
[8] F. Hu, D. Xie, and S. Shen, “On the Application of the Internet of Things in the Field of Medical and Health Care,” in 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, pp. 2053–2058, IEEE, Aug. 2013.
[9] Jaehak Byun, “Smart City Implementation Models Based on IoT Technology”, ASTL, 2016, vol.129, pp. 209-212.
[10] S.-Y. Lien, K.-C. Chen, and Y. Lin, “Toward Ubiquitous Massive Accesses in 3GPP Machine-to-Machine Communications,”IEEE Commun. Mag., vol. 49, no. 4, Apr.2011, pp. 6674.
[11] K. Zheng et al., “The Analysis and Implementation of AllJoyn Based Thin Client Communication System with Heartbeat Function,”Int’l. Conf. Cyberspace Technology, Nov. 2014, pp. 14.
[12] H. Cha, W. Lee, and J. Jeon, “Standardization Strategy for the Internet of Wearable Things,”Int’l. Conf. Information and Commun. Technology Convergence, Oct.2015, pp. 113842.
[13] G. Gardasevic et al., “On the Performance of LoWPAN through Experimentation,”Int’l. Wireless Commun. And Mobile Computing Conf., Aug. 2015, pp. 696701.
[14] IEEE St 802.15.4k-2013, “IEEE Standard for Local and Metropolitan Area Networks Part 15.4: Low-Rate Wireless Personal Area Networks (LRWPANs) Amendment 5: Physical Layer Specifications for Low Energy, Critical Infrastructure Monitoring Networks,”Aug.2013, pp. 1149.
[15] On-Ramp Wireless Inc., “Light Monitoring System Using A Random Phase Multiple Access System,”July 2013.
[16] A. J. Berni and W. Gregg, “On the Utility of Chirp Modulation for Digital Signalling”, IEEE Trans. Commun., vol.21, no. 6, June 1973 pp. 74851.
[17] ETSI, “Electromagnetic Compatibility and Radio Spectrum Matters (ERM); Short Range Devices (SRD); Radio Equipment to Be Used in the 25 MHz to 1 000 MHz Frequency Range With Power Levels Ranging Up to 500mW; Part 1: Technical Characteristics and Test Methods,”tech. rep. EN 300 220-1 V2.4.1, Jan. 2012.
[18] LoRa Alliance, “Lora WAN Specification” V1.0, tech.rep, Jan. 2015.
[19] J. Manyika et al., “The Internet of Things: Mapping the Value Beyond the Hype,”McKinsey Global Institute, tech. rep., June 2015;

Rasika Shinde, Sagar Shinde, "Review on Real Time Approach for Smart Agriculture Based on IoT and Raspberry Pi 3," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.406-411, 2019.

In the era of digital data transmission, relational databases also transmitted through internet for the web base environment and many other important areas, the unauthorized changes to database can lead to sarcastic losses for receiver of the Database. Owners of the database are need to protect and verify originality of their database. Digital watermarking is an effective technical solution to protecting the copyright of databases from wildcat copying by using the built-in properties of relational databases. In this paper, we propose a relational database watermarking scheme that partition the whole database in groups by using the tuple hash value of the database records. After the group formation, we randomize all the tuples of the group and fuse the use of hash byte function over the group tuples along with owner id to form a group string. Similarly, in detection process, we form a string to verify the suspicious watermarked database. The proposed scheme does not require any additional storage in the original database for copyright verification so the approach is robust against the any distortion in original database. Two experiments, Cast comparison and displacement attack conducted over watermark creation and detection algorithms with the group size variation and the results analyzed to show the running cost of the proposed scheme.

ownership protection; group wise partition; tuple hash value; hashbyte

[1] Saraju P. Mohanty , “Digital Watermarking : A Tutorial Review” Indian Institute of Science, Bangalore, 1999
[2] R. Agrawal, J. Kiernan. “Watermarking Relational Databases”, In: Proceeding of the 28th VLDB Conference. Hong Kong, 2002: 155-166.
[3] G.H. Gamal, M.Z. Rashad and M.A. Mohamed “A Simple Watermark Technique for Relational Databa” Mansoura Journal for Computer Science and Information Systems Vol. 4, No.4, Jan2008.
[4] Raju Halder, Shantanu Pal, Agostino Cortesi “Watermarking Techniques for Relational Databases: Survey, Classification and Comparison” Journal of Universal Computer Science, Vol. 16, no.21 2010, pp.3165-3190
[5] Min, Li, Wenyue, Zhao, “An Asymmetric Watermarking Scheme for Relational Database”, Communication Software and Networks (ICCSN), IEEE 3rd International Conference. 2011,pp.180-184
[6] Udai Pratap Rao a, Dhiren R. Patel a, Punitkumar M. Vikani, “Relational Database Watermarking for Ownership Protection 2nd International Conference on Communication, Computing & Security [ICCCS-2012] Science Direct pp.988-995.
[7] B. Wu, et aI., "Design and implementation of spatial data
watermarking service system", Geo-spatial Information Science, vol.13, no. I, pp. 40-48, 2010.
[8] Anuj Kumar Dwivedi ,Dr. B. K. Sharma ,Dr. A. K. Vyas “Relational databases watermarking technique based on embedded proportion” International Education & Research Journal [IERJ] E-ISSN No : 2454-9916 | Volume : 3 | Issue : 6 June 2017 pp 34-36
[9] Anuj Kumar Dwivedi ,Dr. B. K. Sharma ,Dr. A. K. Vyas “Relational Databases Watermarking Technique Based on Specific String Verification” International Journal of Computer Sciences and Engineering [IJCSE] Vol.6(7), Jul 2018, E-ISSN: 2347-2693

Anuj Kumar Dwivedi, B. K. Sharma, A. K. Vyas, "Relational Databases Watermarking Technique Based on Randomized String Verification," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.412-416, 2019.

In last few years, the addiction of internet is apparently recognized as the serious threat to the health of society. This internet addiction gives an impetus to pornographic addiction because most of the pornographic content is accessible through internet. There have been ethical concerns on blocking the contents over internet. In India Uttarakhand High court has taken initiative for the blocking of pornographic content over internet. Technocrats are coming up with various innovative mechanisms to block the content over internet with various techniques. All though long ago in 2015, the Supreme Court of India has already asked to block some of the websites but it could not be materialized. The focus of this research paper is to review the effectiveness of existing web content blocking mechanism of pornographic websites in Indian context.

Pornographic Content, Website Blocking, Blocking Mechanism, Filtering, Error code, Status code

[1]. Aggarwal, A. (2017, July 10). How the Internet of Things is changing the World around Us. Retrieved from https://www.netsolutions.com/insights/how-the-internet-of-things-is-changing-the-world-around-us/
[2]. Alarcón, R. D., Iglesia, J. D., Casado, N., & Montejo, A. (2019). Online Porn Addiction: What We Know and What We Don’t—A Systematic Review. Journal of Clinical Medicine,8(1), 91. doi:10.3390/jcm8010091
[3]. Alwehaibi, H. O. (2015). The Impact Of Using YouTube In EFL Classroom On Enhancing EFL Students Content Learning. Journal of College Teaching & Learning (TLC), 12(2), 121. doi:10.19030/tlc.v12i2.9182
[4]. Gosain, D., Agarwal, A., Shekhawat, S., Acharya, H. B., & Chakravarty, S. (2018). Mending Wall: On the Implementation of Censorship in India. [Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering Security and Privacy in Communication Networks], 418-437. doi:10.1007/978-3-319-78813-5_21
[5]. Here is the full list of 827 porn websites blocked by DoT. (2018, December 15). Retrieved January 24, 2019, from https://indianexpress.com/article/technology/tech-news-technology/here-is-the-full-list-of-827-porn-websites-banned-by-the-dot-5421127/
[6]. Hou, H., Jia, S., Hu, S., Fan, R., Sun, W., Sun, T., & Zhang, H. (2012). Reduced Striatal Dopamine Transporters in People with Internet Addiction Disorder. Journal of Biomedicine and Biotechnology,2012, 1-5.
[7]. InfoByIP.com. (n.d.). Domain and IP bulk lookup tool. Retrieved May 19, 2019, from https://www.infobyip.com/ipbulklookup.php
[8]. Kopf, D., & Kopf, D. (2018, December 13). Forget Netflix-Pornhub tells us everything we need to know about the future of internet viewing habits. Retrieved January 11, 2019, from https://qz.com/1186286/data-show-porn-is-moving-to-mobile/
[9]. Livemint. (2019, March 11). India`s internet base crosses 500 million mark, driven by Rural India. Retrieved April 3, 2019, from https://www.livemint.com/industry/telecom/internet-users-exceed-500-million-rural-india-driving-growth-report-1552300847307.html
[10]. Phillips, D., & Cohen, J. (n.d.). Impact. Retrieved August 17, 2018, from https://www.khanacademy.org/about/impact
[11]. Pti. (2019, March 06). Internet users in India to reach 627 million in 2019: Report. Retrieved April 21, 2019, from https://economictimes.indiatimes.com/tech/internet/internet-users-in-india-to-reach-627-million-in-2019-report/articleshow/68288868.cms
[12]. Register Domains in bulk at GoDaddy. (n.d.). Retrieved May 19, 2019, from https://in.godaddy.com/domains/bulk-domain-search.aspx
[13]. Santoshi, N. (2018, September 28). Unlimited access to porn sites should be curbed: Uttarakhand high court. Retrieved December 9, 2019, from https://www.hindustantimes.com/dehradun/unlimited-access-to-porn-sites-should-be-curbed-uttarakhand-high-court/story-3xBQ8yWjU9rTknhXw43wYN.html
[14]. Singh, K., & Singh, K. (2018, November 30). India is trying to ban porn again. Here`s why it will fail. Retrieved December 15, 2018, from https://qz.com/india/1441110/how-indians-still-visit-pornhub-despite-the-porn-ban/
[15]. Tsur, M. (2014, June 01). Research Confirms Video Improves Learning Results. Retrieved August 24, 2017, from https://www.huffingtonpost.com/michal-tsur/research-confirms-video-i_b_5064181.html
[16]. Www.ETTelecom.com. (2017, June 02). Porn viewing on smartphones up 75% as data rates drop in India - ET Telecom. Retrieved January 21, 2018, from https://telecom.economictimes.indiatimes.com/news/porn-viewing-on-smartphones-surges-75-as-data-rates-drop-in-india/58966755

Saurabh Pandey, Harish Sharma, "Blocking Mechanism of Porn Website in India: Claim and Truth," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.417-428, 2019.

Series Compensation of transmission networks employed with Flexible AC Transmission System device, for transfer of a bulk power, may have to deal with the phenomenon of Sub Synchronous Resonance. The Sub Synchronous Resonance is responsible for generator-turbine shaft damage due to self-excitation, especially in steam and wind turbine power plants, used in radial power systems. After referring about 92, standard research papers, this review paper is prepared, describing the history of Sub Synchronous Resonance problem and basic model developments. The paper demonstrates Induction Generation Effect and Torsional Interaction, both the types of self excitation problems related to Sub Synchronous Resonance. It also demonstrates how the problem of Sub Synchronous Resonance, is dealt in different types of power plants by using Flexible AC Transmission System devices in conjunction with various control techniques. Also some general methods or nonconventional techniques, for SSR mitigation are discussed. The paper is summarizing numerous, Sub Synchronous Resonance analysis techniques and performance of various Flexible AC Transmission System devices for mitigation of sub synchronous oscillations. The paper concludes with scope of research.

Sub Synchronous Resonance (SSR), Flexible AC Transmission System (FACTS), Single Machine Infinite Bus (SMIB), First Benchmark Model (FBM), Second Benchmark Model (SBM), Artificial Neural Network (ANN), Induction Generation Effect (IGE), Torsional Interaction (TI).

[1] L.A. Kilgore, L.C. Eiliott, E.R. Taylor, “The prediction and control of self excited oscillations due to series capacitors in power system”, IEEE Transaction,Vol.90,Issue.3,pp.1305-1311, 1971.
[2] IEEE Committee Report, “Dynamic models for steam and hydro turbines in power system studies”, In the Proceedings of IEEE Power & Energy Society Winter Meeting (PES Win. Meeting 1972), pp.1904-915, 1972.
[3] Paul J. Nolan, Naresh K. Sinha, Robert T.H. Alden, “Eigenvalue sensitivities of power system including network and shaft dynamics”, IEEE Transaction,Vol.95, pp. 1318-1323, 1976.
[4] “First Benchmark Model For Computer Simulation Of Subsynchronous Resonance”, IEEE Transaction,IEEE Committee Report,Vol.96, Issue.5, pp.1565-1572, 1977.
[5] T.H. Putman, D.G. Ramey, “Theory of the modulated reactance solution for subsynchronous resonance”, IEEE Transaction,Vol.101, Issue.6, pp.1527-1535, 1982,.
[6] I. M. Canay, “A novel approach to the torsional interaction and electrical damping of the synchronous machine”, IEEE Transaction,Vol.101, Issue.10, pp.3630-3638, 1982.
[7] N.C. Abi-Samra , R.F. Smith, T.E. Mc Dermott, M.B. Chidester , “Analysis of thyristor- controlled shunt SSR countermeasures”, IEEE Transaction,Vol.104, Issue.3, pp.584-597, 1985.
[8] “Terms, Definitions and Symbols For Subsynchronous Oscillations”, IEEE Transaction,Vol.104,Issue.6,pp.1326-1334, 1985.
[9] “Second Benchmark Model For Computer Simulation Of Subsynchronous Resonance”, IEEE Transaction,Vol.104,Issue.5, pp.1057-1066, 1985.
[10] Eli Katz, Jim Tang, “Comparison of SSR Calculations and Test Results” IEEE Committee Report, IEEE Transaction,Vol.4,Issue.1, pp.336-344, 1989.
[11] “READER’S GUIDE TO SUBSYNCHRONOUS RESONANCE”, IEEE Committee Report, IEEE Transaction,Vol.7,Issue.1, pp.150-157, 1992.
[12] J.B.Ekanayake, N. Jenkins, “A three-level Advanced Static VAr Compensator”,IEEE Transaction,Vol.11,Issue.1,pp.540-545, 1996.
[13] Hermann W. Dommel, “Digital computer solution of electromagnetic transients in single and multiphase networks”, IEEE Transaction,Vol.104, Issue.5,pp.388-399, 1969.
[14] Narain G. Hingorani, “A new scheme for sub synchronous resonance damping of torsional oscillations and transient torque -part-I”, PER, pp.1852-1855, 1981.
[15] Narain G. Hingorani”,A New Scheme for Subsynchronous Resonance Damping of Torsional Oscillations and Transient Torque-Part-II”, PER, pp.1856-1857, 1981.
[16] Pouyan Pourbeik, Donald G Ramey, Nicholas Abi-Samra, Deniel Brooks, Anish Gaikwad, “Vulnerability of large steam turbine generators to toorsional interactions during electrical grid disturbances”, IEEE Transaction,Vol.22,Issue.3,pp.1250-1258, 2007.
[17] Wei Duan, Zhangqi Wang, “Vibration reliability analysis of turbine blade based on ann and monte carlo simulation”, In the Proceedings of IEEE (ICNC 2010), Valencia,Spain,pp.1934-1939, 2010.
[18] Chanxia Zhu, Lingling Fan, Minqiang Hu, “Control and analysis of DFIG-based wind turbines in a series comensated network for ssr damping”, In the Proceedings of IEEE Power & Energy Society General Meeting (PES 2010), Detroit, Michigan, USA,pp.1-6, 2010.
[19] Lingling Fan, Rajesh Kavasseri, Zhixin Lee Miao, Chanxia Zhu, “ Modelling Of DFIG- Based Wind Farms for SSR Analysis”, IEEE Transaction,Vol.25, Issue.4, pp.2073-2082, 2010.
[20] Zhixin Miao, “Impedance Model Based SSR Analysis for Type 3 Wind Generator and Series Compensated Network”,Vol. 27, Issue. 4, pp.984-991, 2012.
[21] D.H.R. Suriyaarachchi, U.D. Annakage, C. Karawita, D.A. Jacobson, “ A procedure to study sub-synchronous interactions in wind integrated power system”, IEEE Transaction,Vol.28,Issue.1, pp. 377-384, 2013.
[22] Rajiv K. Verma, Akshaya Moharana, “SSR in double-cage induction generator based wind farm connected to series- compensated transmission line”, IEEE Transaction,Vol.28, Issue.3,pp.2573-2583, 2013.
[23] Hossein Ali Mohammadpour, Enrico Santi, “Modelling and control of gate- controlled series capacitor interfaced with a dfig-based wind farm”, IEEE Transaction,Vol.62,Issue.2,pp.1022-:1033, 2015.
[24] Hossein Ali Mohammadpour, M Moinul Islam, Enrico Santi, Yong-June Shin, “SSR damping in fixed speed wind farms using series FACTS controllers”, IEEE Transaction,Vol.31,Issue.1, pp.76-86, 2016.
[25] Hossein Ali Mohammadpour, Enrico Santi , “SSR damping controller design and optimal placement in rotor-side and grid-side converters of series- compensated DFIG-based wind farm”, IEEE Transaction,Vol.6,Issue.2, 2015.
[26] K.R. Padiyar, “Analysis of Subsynchronous Resonance in Power System”, Boston, MA, USA: Kluwer Academic,1999.
[27] K.R. Padiyar, “Power System Dynamics, Stability & Control, Hyderabad”, India: B S Publications, 2008.
[28] P. Kundur, “Power System Dynamics, Stability & Control”, New York, NY, USA: McGraw-Hill, 1994.
[29] P. M. Anderson, “Subynchronous Resonance in Power System”, New York, NY, USA: IEEE Press, 1990.
[30] Khaled Mohammad Alawasa, Wilsun Xu, Fellow, “ New approach to damp subsynchronous resonance by reshaping the output impedance of voltage-sourced converters”, In Proceedings of IEEE Power & Energy Society General Meeting (PES Gen. Meeting 2013),pp.1-5, 2013.
[31] Luke Livermore, Carlos E Ugalde-Loo, Qing Mu, Jun Liang1, Janaka B Ekanayake1,Nick Jenkins1, “Damping of subsynchronous resonance using avoltage source converter-based high-voltage directcurrent link in a series-compensated Great Britain transmission network”, IET Gen., Transm. Distrib.,Vol.8, Issue.3, pp.542–551, 2014.
[32] Khaled Mohammad Alawasa, Wilsun Xu, Fellow, “ A simple approach to damp ssr in series compensated system via reshaping the output admittance of a nearby VSC-based system”, IEEE Transaction,Vol.62, Issue.5, pp.2673-2682, 2015.
[33] K.R. Padiyar, Senior Member, IEEE, and Nagesh Prabhu, “Design and performance evaluation of subsynchronous damping controller with STATCOM”, IEEE Transaction,Vol.21,Issue. 3,pp.1398-1405, 2006.
[34] Nagesh Prabhu; M. Janaki; R. Thirumalaivasan, “Damping of subsynchronous resonance by subsynchronous current injector with STATCOM”, In the Proceedings of IEEE Conference on Convergent Technologies(TENCON 2009),Singapore,pp.1-6, 2009.
[35] MSEI Moursi, Vinod Khadkikar, “Novel control strategies for ssr mitigation and damping power system oscillations in a series compensated wind park”, In the Proceedings of Annual Conference of the IEEE Industrial Electronics Society (IECON 2012), Montreal, Quebec, Canada,pp.5335-5342, 2012.
[36] Akshaya Moharana, Rajiv K. Verma, Ravi Seethapathy, “SSR alleviation by STATCOM in induction- generator based wind farm connected to series compensated line”, IEEE Transaction,Vol.5, Issue.3, pp.947-957, 2014.
[37] Nagesh Prabhu; M. Janaki; R. Thirumalaivasan, “Damping of subsynchronous resonance by subsynchronous current injector with STATCOM” , In the Proceedings of IEEE Conference on Convergent Technologies(TENCON 2003),Bangalore,India, pp.1-6, 2009.
[38] Fawzi A. Rahman AI Jowder, Boon-Teck Ooi “Series compensation of radial power system by a combination of SSSC and dielectric capacitors”, IEEE Transaction,Vol.20,Issue.1,pp. 458-465, 2005.
[39] K.R. Padiyar ,Nagesh Prabhu “,Analysis of subsynchronous resonance with three level twelve-pulse VSC based SSSC”, In the Proceedings of IEEE Conference on. Convergent Technologies (TENCON 2003),Bangalore,India,pp.76 – 80, 2003.
[40] Nagesh Prabhu, R Thirumalaivasan ,M Janaki, “Design and performance evaluation of subsynchronous current suppressor with SSSC” In the Proceedings of IEEE (TENCON 2009), Suntec City, Singapore,pp.1-6, 2009.
[41] D. Rai, , S.O. Faried, G. Ramakrishna and A. Edris, “Impact of imbalanced phase operation of SSSC on damping subsynchronous resonance”, In the Proceedings of IEEE Power & Energy Society General Meeting (PES Gen. Meeting 2011) Detroit,USA, pp.1-7, 2011.
[42] K.R. Padiyar , AMKulkarni, “Control design and simulation of unified power flow controller”, IEEE Transaction,Vol.13,Issue.42, pp.1348–1354, 1998.
[43] Sajjad Golshannavaz, Farrokh Aminifar, and Daryoush Nazarpour, “Application of UPFC to enhancing oscillatory response of series-compensated wind farm integrations”, IEEE Transaction,Vol.5, Issue.4, 2014.
[44] K.R. Padiyar and N. Prabhu, “Investigation of SSR characteristics of unified power flow controller”,ELSEVIER,EPSR,Vol.74, Issue.2,pp.211–221, 2005.
[45] L. Gyugyi, T.R. Rietman A. Edris, “The unified power flow controller: New approach to power transmission control”, IEEE Transaction,Vol.10, Issue.2,pp.1085–1097, 1995.
[46] Wang. L, Hsu. Y., “Damping of subsynchronous resonance using excitation controllers and static VAR compensations: a comparative study”, IEEE Transaction,Vol.3,Issue.1,pp.6–13, 1988.
[47] Hailian Xie, Marcio, M de Oliveira, “Mitigation of ssr in presence of wind power and series compensation by SVC”, In the Proceedings of Int. Conf. on Power System Tech.,(POWERCON 2014), Chengdu,China,pp.2819-2826, 2014,.
[48] S. A. Khaparde, V. Krishna, “Simulation of Unified Static VAR compensator and power system stabilizer for arresting subsynchronous resonance”,IEEE Transaction,Vol.14,Issue.3, pp.1055-1062, 1999.
[49] LASZLOGYUGYI, “Reactive power generation and control by thyristor circuits”, IEEE Transaction,Vol.15, Issue.5,pp.521-532, 1979.
[50] A. E. Hammad, MEI- Sadek, “Application of a thyristor controlled VAR compensator for damping subsynchronous oscillations in power system”, IEEE Transaction,Vol.103, Issue.1, pp.198-212, 1984.
[51] J. Urbanek, R. J. Piwko, EV Larsen, B. L. Damsky, B. C. Furumasu, W. Mittlestadt. , “Thyristor controlled series compensation prototype installation at the Slatt 500kv substation”, IEEE Transaction,Vol.8, Issue.3, pp. 1460-1469, 1993.
[52] Rajesh Rajaraman, Ian Dobson, Robert H Lasseter, Yilchih Shern, “Computing the damping of subsynchronous oscillations due to a thyristor controlled series capacitor” , IEEE Transaction, Vol.11, Issue.2, pp.1112-1119, 1996.
[53] R. J. Piwko, C. A. Wegner, S. J. Kinney, J. D. Eden, “Subsynchronous resonance performance tests of the Slatt thyristor- controllled series capacitor”, IEEE Transaction,Vol.11, Issue.2,pp.1112-1119, 1996.
[54] Shou Bao Zou, Dariusz C. Zarkowski, “A novel phase- control topology to improve characteristics of thyristor controlled compensation in AC transmission system”, In the Proceedings of IEEE Power & Energy Society (PES Summer Meeting 1999), Edmonton, Alberta, Canada,pp. 1134-1119, 1999.
[55] Y. Tanaka, HTaniguchi, MEgawa, “Using miniature model and EMTP simulations to evaluate new methods to control and protect a thyristor controlled series compensator”, In the Proceedings of IEEE Power & Energy Society (PES Summer Meeting 1999), Edmonton, Alberta, Canada, pp.1196-1201, 1999.
[56] Carlos Gama, Ricardo Tenorio”,Improvements for power system performance: Modelling, analysis and benefits of TCSC”, In the Proceedings of IEEE Power & Energy Society (PES Winter Meeting 2000), pp.1462-1467, 2000.
[57] Tang Yi, Yu Rui-quin, “Mechanism analysis of using thyristor controlled series compensation to mitigate subsynchronous resonance”,In the Proceedings of IEEE International Power Electronics Conference(IPEC 2010), Sapporo, Japan,pp.1094-1099,2010.
[58] Khosro Kabiri, Sebastian Henschel a, Hermann W Dommel, “ Resistive Behaviour of Thyristor- Controlled Series Capacitors at Subsynchronous Frequencies”,IEEE Transaction,Vol.19,Issue.1, pp.374-379, 2004.
[59] S. Sajedi, F Khalifeh, T Karimi, Z Khalifeh, “A New Method for Subsyncronous Damping Using TCSC”, Australian Journal Of Basic and Applied Sciences,pp. 875-881, 2011.
[60] Xiang Zheng, Zheng Xu, Jing Zhang, “ A supplementary Damping Controller of TCSC for Mitigating SSR”, In the Proceedings of IEEE Power & Energy Society General Meeting (PES Gen. Meeting 2009), Calgary, Alberta,Canada, pp.1-5, 2009.
[61] F.D. Jesus, E. H. Watanbe , L. F. W. Souza, F .D. Jesus, “Analysis of SSR mitigation using gate-controlled series capacitors”, In the Proceedings of IEEE Power Electronics Specialists 2005(PESC 2005), Recife, Brazil,pp.1402-1407, 2005.
[62] A. Binaco, “GCSC-Gate Controlled Series Capacitor: A New FACTS device for Series Compensation of Transmission Lines”, In the Proceedings of IEEE Transmission and Distribution Conference and Exposition 2004(ITDCE 2004), Chicago, IL, USA,pp.981-986, 2004.
[63] F.D. de Jesus ; E.H. Watanabe ; L.F.W. Souza ; J.E.R. Alves, “SSR Mitigation Using Gate-Controlled Series Capacitors”, In the Proceedings of IEEE(Power & Energy Society General meeting2006), Montreal, Quebec, Canada, pp.7, 2006.
[64] Swakshar Ray,Ganesh Venayagamoorthy, “Nonlinear Modified PI Control Of Multi-Module GCSCs in a Large Power System”, In the Proceedings of IEEE Industry Applications Conference Forty-First IAS Annual Meeting (IAS Annual meeting 2006), pp.1345-1351, 2006.
[65] Fabio Domingues de Jesus, Edson Hirokazu Watanbe, Luiz Felipe Willcox de Souza, Jose Eduardo RAlves “SSR and Power Oscillation Damping Using Gate- Controlled Series Capacitors” IEEE Transaction,Vol.22, Issue.3, pp.1806-1812, 2007.
[66] B. S. Umre, J. B. Helonde, J. P. Modak, Sonali Renkey (Rangari), “Application of Gate- Controlled Series Capacitors (GCSC) for Reducing Stresses due to Sub-Synchronous resonance in Turbine- Generator Shaft”, In the Proceedings of IEEE Energy Conversion Congress & Exposition (ECCE 2010), Atlanta, Georgia,pp.2300-2305, 2010.
[67] Hossein Ali Mohammadpour, Enrico Santi, “Modelling and Control of Gate-Controlled Series Capacitor Interfaced With a DFIG- Based Wind Farm”,IEEE Transaction,Vol.62,Issue.2, pp.1022-1033, 2015.
[68] K. R. Padiyar, K. Uma Rao”,Discrete Control of series compensation for stability improvement in power system”, In the Proceedings of IEEE International Conference on Control Application (ICCA 1995),pp.246-25, 1995.
[69] Fahd A Alturki, Adel Ben Abdennour “Neuro-Fuzzy Control of a steam Boiler-Turbine Unit” , In the Proceedings of IEEE International Conference on Control Application(ICCA 1999), pp.1050-1055, 1999.
[70] Sasidharan Sreedharan, J G Singh, I Made Wartana, Kittavit Buayai “,Development of PSO Based Control Algorithms for Maximizing Wind Energy Penetration”, In the Proceedings of IEEE Power & Energy Society Gen. Meeting (PES Gen. Meeting 2011), Detroit, Michigan, USA. pp.1-6, 2011.
[71] Yu Xia, Henian Xia”,Application of Modern Techniques for Detecting Subsynchronous Oscillations in Power System”, In the Proceedings of IEEE Power & Energy Society General Meeting(PES Gen. Meeting 2013),Vancouver,B.C.,Canada, pp.1-5,2013.
[72] Xiaoliang Dong, Xiaorong Xie, Yingduo Han,Liang Wang, Dawei Sun”,Coordinated Parameters Design of SEDC and GTSDC for SSR Mitigation” In the Proceedings of IEEE Power & Energy Society General Meeting(PES Gen. Meeting 2014),National Harbor, Washington, D.C.,pp.1-5, 2014.
[73] Xinyao Zhu, Haishun Sun, Jintu Wen, Shijie Cheng “,Improved Complex Torque Coefficient Method Using CPCM for Multi- Machine System Analysis”, IEEE Transaction,Vol.29, Issue.5,pp.2060-2068, 2014.
[74] Yesar, Kucukefe, Adnan Kaypmaz, “Damping Subsysncronous Resonance Oscillations by Delayed feedback Control”, In the Proceedings of IEEE Power & Energy Society General Meeting (PES 2010), Minneapolis, Minnesota, USA, pp.1-7, 2010.
[75] Atia Adrees, Jovica V Milanovic, “Optimal Compensation of Transmission Lines Based On Minimisation of the Risk of Subsynchronous Resonance”, IEEE Transaction,Vol.31,Issue.2, pp.1038-1047, 2016.
[76] Po-Hsu Huang, Mohammed Shawky, EI Moursi, Weidong Xiao, James L Kirtley”,Subsynchronous Resonance Mitigation for series- Compensated DFIG-Based Wind Farm by Using Two-Degree-Of Freedom Control Strategy”,IEEE Transaction,Vol.30,Issue.3, pp.1442-1454, 2015.
[77] H. Bora Karayaka Ali Keyhani, Gerald Thomas Heydt, Baj L Agarwal , Douglas A Selin, “Neural Network based Modelling of a large steam turbine-Generator Rotor BodyParameters from on-Line Disturbance Data”, IEEE Transaction,Vol.16,Issue.4, pp.305-311, 2001.
[78] H. Bora Karayaka, Ali Keyhani, Baj LAgarwal, Doughlas A Selin, Gerald Thomas Heydt, “Identification of Armature, Field and Saturated Parameters of a large Steam Turbine-Generator from Operating Data”, IEEE Transaction,Vol.15,Issue.2, pp.181-187, 2000.
[79] Quiang Fu, Yi Shen, Jian Qiu Zhang, Shengli Liu “An Approach to Fault Diagnosis Based On a Hierarchical Information Fusion Scheme”, In the Proceedings of IEEE Instrumentation and Measurement Technology Conference(IMTC 2001),Budapest, Hungary, pp.875-878, 2001.
[80] Chin-Pao Hung, Mang-Hui Wang, Chin-Hsing Cheng, Wen-Lang Lin, “Fault Diagnosis of Steam Turbine- Generator Using CMAC Neural Network Approach”, In the Proceedings of IEEE International Joint Conference on Neural Networks(IJCNN 2003), Portland, USA,pp.2988-2993, 2003.
[81] Un-Chul Moon, Kwang Y Lee”,A Boiler-Turbine System Control Using A Fuzzy Auto-Regressive Moving Average(FARMA) Model”, IEEE Transaction,Vol.18, Issue.1, pp.142-148, 2003.
[82] Edward W.B. Lo, Huafeng Liu, Pencheng Shi”,Hɷ Filtering And Physical Modeling For Robust Kinematics Estimation”, In the Proceedings of IEEE International Conference on Image Processing(ICIP 2003),Barcelona,Catalonia,Spain,pp.169-172, 2003.
[83] Qinglin Guo, Ming Zhang, “A Novel Approach for Fault Diagnosis Of Steam Turbine Based On Neural Network And Genetic Algorithm”, In the Proceedings of IEEE International Joint Conference on Neural Networks(IJCNN 2008),Hong Kong, China,pp.25-29, 2008.
[84] Jong S. Kim, Kody M Powell, Thomas F Edgar, “Nonlinear Model Predictive Control For a Heavy Duty Gas Turbine Power Plant”, In the Proceedings of American Control Conference (ACC 2013), Washington, DC, USA,pp.2952-2957, 2013.
[85] Jan Aril d Wiik, Fransisco Danang Wijaya and Ryuichi Shimada, “Characteristics of the Magnetic Energy Recovery Switch (MERS) as a Series FACTS Controller”, IEEE Transaction,Vol.24, Issue.2, pp.828-836, 2009.
[86] Narain G. Hingorani, “A New Scheme for Subsynchronous Damping of Torsional Oscillations and Transient Torque- Part I”, IEEE Transaction,Vol.100, Issue.4, pp.1852-1855, 1981.
[87] Zhao Xueqiang, Chen Chen, “Damping Subsynchronous Resonance Using an Improved NGH SSR Damping scheme”, IEEE Transaction,PESS,pp.780-785, 1999.
[88] Yu Yang, Xiaolin Li, Zilong Mu, Xingyuan li, Yuhong Wang, “Rapid Risk Assessment of SSR Caused by Series Capacitors Compensations in Large Power Grid “, In the Proceedings of IEEE Asia-Pacific Power and Energy Engineering Conference (APPEEC 2010), Chengdu, China, pp.1-4, 2010.
[89] Xu Jinfeng, Song Ruihua, Xiang Zutao, Wang Xiaojun, Du Ning, “Simulation Studies on Mitigating Subsynchronous Resonance with Coaxial Double Fed motor”, In the Proceedings of IEEE International Conference on Power System Technology (POWERCON 2012), Auckland,New Zealand, pp.1-4, 2012.
[90] Gajanan V. Gotmare,Vasudeo B. Virulkar, “Eigenvalue Analysis of Subsynchronous Resonance Study in Series Compensated Wind Farm”, International Journal of Modern Trends in Engineering and Research, IJMTER,Vol.2, Issue.2, pp.267-275, 2015.
[91] Ajinkya Pachghare, “ R.M.SaharVoltage regulation and reactive power compensation by SSSC based on 48-pluse GTO (VSC)”, International Journal of Scientific Research in Computer Science and Engineering, ISROSET,Vol.5, Issue.4, pp.41-44, 2017.
[92] Sabbella. V.V.S.K. Reddy, K. Satyanarayana, “Sensing of Ground Fault in Bipolar LVDC Grid”, IJSRNSC,Vol.6, Issue.6,pp.19-24, 2018.

P. D. Giri, C.D. Kotwal, "A Review on SSR Analysis of FACTS Compensated Power System Using Various Control Techniques," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.429-442, 2019.

The k-means algorithm has been known as clustering techniques. It is used in various fields and domains such as medical imaging as well as biometrics fields etc. Although there are several optimums clustering mechanism in existence, the objective of paper is to discuss the clustering technique especially Kmean clustering. It has been observed that there are many researches already done in field of K-MEAN clustering. The issues related to Kmean clustering would be discussed in this research. Research has introduced the more effective and optimised cluster mechanism to classify the data set into various clusters.

Clustering, Fuzzy, K-MEAN Clustering

[1]. S. S. R. Abidi, "A data mining strategy for inductive data clustering: a synergy between self-organising neural networks and K-means clustering techniques," 2000 TENCON Proceedings. Intelligent Systems and Technologies for the New Millennium (Cat. No.00CH37119), Kuala Lumpur, Malaysia,
[2]. D. Napoleon, "An efficient K-Means clustering algorithm for reducing time complexity using uniform distribution data points," Trendz in Information Sciences & Computing(TISC2010), Chennai, 2010, pp. 42-45.
[3]. S. H. Ganesh, "A novel priority based data mining algorithm using improved K-means clustering for detecting protein sequence from dataset," 2010 IEEE International Conference on Computational Intelligence and Computing Research, Coimbatore, 2010, pp. 1-4.
[4]. S. Sitanggang, "K-means clustering visualization of web-based OLAP operations for hotspot data," 2010 International Symposium on Information Technology, Kuala Lumpur, 2010, pp. 1-4.
[5]. S. Na, "Research on k-means Clustering Algorithm: An Improved k-means Clustering Algorithm," 2010 Third International Symposium on Intelligent Information Technology and Security Informatics, Jinggangshan, 2010, pp. 63-67.
[6]. K. A. A. Nazeer, "Enhancing the K-means Clustering Algorithm by Using a O(n log n) Heuristic Method for Finding Better Initial Centroids," 2011 Second International Conference on Emerging Applications of Information Technology, Kolkata, 2011, pp. 261-264.
[7]. R. V. Singh, "Data clustering with modified K-means algorithm," 2011 International Conference on Recent Trends in Information Technology (ICRTIT), Chennai, Tamil Nadu, 2011, pp. 717-721.
[8]. D. V. S. Shalini, “Mining frequent patterns of stock data using hybrid clustering," 2011 Annual IEEE India Conference, Hyderabad, 2011, pp. 1-4.
[9]. N. Aini Abd Majid, "K-means clustering pre-analysis for fault diagnosis in an aluminium smelting process," 2012 4th Conference on Data Mining and Optimization (DMO), Langkawi, 2012, pp. 43-46.
[10]. T. Soni Madhulatha. AN OVERVIEW ON CLUSTERING METHODS. IOSR Journal of Engineering Apr. 2012, Vol. 2(4) pp: 719-725.
[11]. W.Sarada, A REVIEW ON CLUSTERING TECHNIQUES AND THEIR COMPARISON , International Journal of Advanced Research in Computer Engineering &Technology (IJARCET) Volume 2 Issue 11, November 2013
[12]. Bhoj Raj Sharmaa Clustering Algorithms: Study and Performance Evaluation Using Weka Tool.International Journal of Current Engineering and Technology ISSN 2277 - 4106 © 2013.
[13]. Shweta Srivastava. “Clustering Techniques Analysis for Microarray Data.” International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology IJCSMC, Vol. 3, Issue. 5, May 2014
[14]. Muhammad Husain Zafar. A Clustering Based Study of Classification Algorithms. International Journal of Database Theory and Application Vol.8, No.1 (2015), pp.11-22.
[15]. N. Claypo "Opinion mining for thai restaurant reviews using K-Means clustering and MRF feature selection," 2015 7th International Conference on Knowledge and Smart Technology (KST), Chonburi, 2015, pp. 105-108.
[16]. S. Kapil, "On K-means data clustering algorithm with genetic algorithm," 2016 Fourth International Conference on Parallel, Distributed and Grid Computing (PDGC), Waknaghat, 2016, pp. 202-206.
[17]. S. Kapil and M. Chawla, "Performance evaluation of K-means clustering algorithm with various distance metrics," 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), Delhi, 2016, pp. 1-4.
[18]. R. Ahlawat, S "Analysis of factors affecting enrollment pattern in Indian universities using k-means clustering," 2016 International Conference on Information Technology (InCITe) - The Next Generation IT Summit on the Theme - Internet of Things: Connect your Worlds,
[19]. V. Baby, "Distributed threshold k-means clustering for privacy preserving data mining," 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Jaipur, 2016, pp. 2286-2289.
[20]. Saini, J. Minocha, "New approach for clustering of big data: DisK-means," 2016 International Conference on Computing, Communication and Automation (ICCCA), Noida, 2016, pp. 122-126.
[21]. Qi, Y. Yu, "K*-Means: An Effective and Efficient K-Means Clustering Algorithm," 2016 IEEE International Conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom) (BDCloud-SocialCom-SustainCom), Atlanta, GA, 2016, pp. 242-249.
[22]. R. Condrobimo, "Data mining technique with cluster anaysis use K-means algorithm for LQ45 index on Indonesia stock exchange," 2018 International Conference on Information and Communications Technology (ICOIACT), Yogyakarta, 2018, pp.
[23]. M. Aryuni, "Customer Segmentation in XYZ Bank Using K-Means and K-Medoids Clustering," 2018 International Conference on Information Management and Technology (ICIMTech), Jakarta, 2018, pp. 412-416.

Saumya Kumar, Neetu Verma, "Resolving Issues of Empty Cluster Formation in KMEAN Algorithm Using Advanced Approach," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.443-448, 2019.