Volume-7 , Issue-6 , Jun - 2019 Go Back

Presently multi day apportion card is incredibly imperative for each home and utilized for different eld, for example, relatives subtleties, to get gas association, it execute as location proof for different purposes and so on. Every one of the general population having a proportion card to get the different materials (sugar, rice, oil, lamp oil, and so on) from the proportion shops. RFID cards are given rather than customary apportion cards, this RFID tag contains all data of card holder like Aadhar no., thumb impression, iris impression and so on. Shrewd card based programmed proportion shop is novel methodology in open circulation system (PDS) significant for progressively beneficial, exact, and mechanized procedure of extent conveyance. In proposed framework customer needs to enlist with the entry where he is designated with client ID and Password which are open from email ID. At the point when customer visits the proportion shop, he/she will filter RFID tag before RFID per user. Burden cell and IR sensor is utilized for exact weighing of grain and fluid individually.

RFID Tag, Node MCU, PIC, Ration card

[1] Harshali P. Rane, Kavita S. Patil, AditiS. Chaudhari, Priyanka M.Pendharkar, “Automated Rationing System Using Raspberry Pi”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 5, Issue 4, April 2017
[2] Kumbhar Aakanksha, Kumavat Sukanya, Lonkar Madhuri, Mrs. A.S. Pawar, “Smart Ration Card System Using Raspberry-pi”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 5, Issue 4, April 2016
[3] S.Valarmathy, R.Ramani, Fahim Akhtar, S.Selvaraju, G.Ramachandran “Automatic Ration Material Distributions Based on GSM and RFID Technology”, I.J. Intelligent Systems and Applications, 2013, 11, 47-54, October 2013.
[4] Kashinath Wakade, Pankaj Chidrawar, Dinesh Aitwade, “Smart Ration Distribution and Controlling”, International Journal of Scientific and Research Publications, Volume 5, Issue 4, April 2015.
[5] Rashmi Pandhare, Mayur Rewatkar, Nikita Meghal , Nikhil Bondre, Ashvini Ambatkar ,Akshaya Dole, “Modern Public Distribution System for Digital India”, International Research Journal of Engineering and Technology (IRJET) Volume: 03 Issue: 03 | Mar-2016.
[6] Survey on Smart Ration Card using Internet of Things Aarti Bhosale Shweta Bhor Pratima Sabale Pushpak Shinde International Journal of Computer Applications (0975 – 8887) Volume 180 – No.3, December 2017
[7] Chetan S. Kandare Trimbakeshwar Nasik Vaishali R. Tribhuvan Smart Application using Biometric and RFID for Ration Card International Journal of Computer Applications (0975 – 8887) Volume 177 – No.4, November 2017
[8] Golden Bagul1, Brendon Desouza2, Tejaswini Gaikwad3, Ankush Panghanti4, Trupti Kumbhare A Survey on Smart Ration Card System International Journal Of Engineering And Computer Science Volume 6 Issue 1 Jan. 2017, Page No. 20096-20098
[9] Prashant Kontam1, Ajinkya Tarlekar2, Akshay Deshmukh3, Vivek Kale4, Prof.Sachin Patil5 A Review on Smart Ration Card System International Journal of Innovative Research in Computer and Communication Engineering Vol. 5, Issue 3, March 2017
[10] Ms. Kritika Patil1 , Ms. Monica Sundrani2, Ms. Sweta Kumari3, Ms. Aditi Kakde4, Prof. Mahesh Gosavi Smart Ration Card System Based on GSM Technique International Research Journal of Engineering and Technology (IRJET) Volume: 03 Issue: 11 | Nov -2016 2016, Page 1318

Supriya Lokhande, Sagar Shinde, "Smart Ration Distribution System," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.1158-1161, 2019.

the present DNS is a distributed network which helps in finding the IP addresses. The ICANN or the Internet Corporation for Assigned Names and Numbers lays the regulation for the functioning of DNS. It gives approval for the TLD or the Top Level Domain names like .com. It is the authority that accredit the registrars like the GoDaddy to sell the rights of using the domain name. The current DNS system is hierarchical. This system’s root servers represents a high-value attack vector. Since the entire system is centralized even the slightest failure at a single point can take down the whole internet. With a DNS of the Blockchain, it will be based on the decentralized system, and thus, it may not hamper the redirection process. Furthermore, Blockchain based DNS may counter the censure and also avoid the problem of cache poisoning or DNS spoofing. It is true that Blockchain-based DNS inherits the benefits of decentralization. Unlike the current DNS system which is governed and is controlled by organizations, Blockchain-based DNS does not have any authorities. Every node in the server is equal. Only the owners can make changes in the current records. It is difficult for the authorities to make any changes in the domain name records. The current DNS system is prone to attach and hacking, but this is not the case with Blockchain based DNS. Blockchain based DNS will host or store DNS records on blockchain in the form of blocks which means that every block will store domain name and its corresponding IP address (name, value pair).

DNS, DNSSEC, blockchain, Bitcoin, Namecoin, Blockstack

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[16] WILCOX-O`HEARN Z. Names: decentralized, secure, human meaningful: Choose two [EB/OL]. https://web.archive.org/web/ 20011020191610/http://zooko.com/distnames.html.
[17] DINGLEDINE R, MATHEWSON N, SYVERSON P. Tor: the second-generation onion router [J]. Journal of the Franklin Institute, 2004, 239(2): 135-139.
[18] Medium. (2019). EthDNS: an Ethereum backend for the Domain Name System - Medium. [Online] Available at: https://medium.com/@jgm.orinoco/ethdns-an-ethereum-backend-for-the-domain-name-system-d52dabd904b3 [Accessed 25 Jun. 2019].
[19] Zhu Guo-ku, Jiang Wen-bao.A Decentralized Domain Name System for the Network [J]. Syberspace Security, 2017(1):14-18.
[20] Yuan Yong, Wang Fei-yue. Blockchain: the state of the art and future trends [J]. Acta Automatica Sinica, 2016, 42(4):481-494
[21] Zhao He, Li Xiaofeng,Zhan Likui,et al. Data integrity protection method for microorganism ampling robots based on blockchain technology[J].Journal of Huazhong University of Science and Technology(Natural Science Edition),2015(S1):216-219.
[22] Wang Jiye, Guo Lingchao,Dong Aiqiang,et al. Bolck Chain Based Data Security Sharing Network Architecture Sesearch[J].Journal of Computer Research and Development ,2017,54(4):742-749.
[23] Swan M. Blockchain Thinking: The Brain as a Decentralized Autonomous Corporation [Commentary][J]. IEEE Technology & Society Magazine, 2015, 34(4):41- 52.
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[25] Zyskind G, Nathan O, Pentland A`. Decentralizing Privacy: Using Blockchain to Protect Personal Data[C]// IEEE Security and Privacy Workshops. IEEE, 2015:180- 184.
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[29] Blockstack: A New Internet for Decentralized Applications Muneeb Ali, Ryan Shea, Jude Nelson Michael J. Freedmany http://blockstack.org Whitepaper Version 1.1 October 12, 2017.
[30] Nebulis[EB/OL].https://www.nebulis.io/.
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[33] Ethereum project [EB/OL]. https://www.ethereum.org/.
[34]Emercoin[EB/OL].http://emercoin.com/DNS_and_Name-Value_ Storage.
[35] Ethereum decentralized DNS [EB/OL]. http://etherid.org/.
[36] L. Liu and B. Xu, “Research on information security technology based on blockchain,” 2018 IEEE 3rd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), 2018.
[37] B. Benshoof, A. Rosen, A. G. Bourgeois, and R. W. Harrison, “Distributed Decentralized Domain Name Service,” 2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), 2016.
[38] K. Kim, Y. You, M. Park, and K. Lee, “DDoS Mitigation: Decentralized CDN Using Private Blockchain,” 2018 Tenth International Conference on Ubiquitous and Future Networks (ICUFN), 2018.
[39] J. Liu, B. Li, L. Chen, M. Hou, F. Xiang, and P. Wang, “A Data Storage Method Based on Blockchain for Decentralization DNS,” 2018 IEEE Third International Conference on Data Science in Cyberspace (DSC), 2018.

Aabid Hussain Ganai, Mir Aman Sheheryar, "Decentralization of DNS using Blockchain: A Survey," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.1162-1168, 2019.

Poor security of Smart homes resulting from compromised system password and IP address has been on the increase as a result of hackers’ access to system. For this reason, there is need to introduce tertiary security feature. This work presents comprehensive survey of security challenges in smart home intrusion detection systems using qualitative research methodology. The researcher provided design of tertiary security parameter- Soft token along side IP address and password that serve as primary and secondary parameters to optimize system. Object oriented analysis and design plan was similarly embraced to help indicate the relationship between object and its class. K-means algorithm as data mining clustering technique was used to aid intrusion detection and prevention in smart home i.e. the system was able to differentiate between authorized from unauthorized access and simultaneously, send security warning to the framework administrator’s email whenever there is an intrusion. The development stage was done using some sets of software tools: PHP, HTML, JavaScript, and MySQL database system. Xampp server was used to test-run the system during the development process. Experimental result shows that soft token alongside IP address and password to check for an intrusion was able to optimize security.

Smart Home, Intrusion Detection systems, K-Means algorithm, Password, Security, Soft token

[1] S. Chitnis, N. Deshpande & A. Shaligram, “An Investigative Study for Smart home security: Issues, challenges and countermeasures”, Wireless Sensor Network, 8(04), 61, 2016.
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[3] M. Kumar & K. Shinde, “Technical Report on Intruder Detection and Alert System”, arXiv preprint arXiv:1509.09138, 2015.
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[12] M. Gauger, D. Minder, P. J. Marron, A. Wacker & A. Lachenmann, “Prototyping Sensor-Actuator Networks for Smart home”, In Proceedings of the workshop on Real-world wireless sensor networks pp. 56-60, 2008.
[13] N.K Denzin & Y.S. Lincoln, “The Sage Handbook of Qualitative Research”, Sage Publications 2017.
[14] E. Colbert, “Requirement Analysis with the Object Oriented Software Development Method”, 1998.
[15] N. Rehman, “Data Mining techniques methods Algorithms and Tools”, International Journal of Computer Sciences and Mobile Computing, Vol. 6 Issue 7, pg 227-231, 2017.
[16] P. Rutravigneshwaran, “A Study of Intrusion Detection System using Efficient Data Mining Techniques”, International Journal of Scientific Research in Network security and Communication, Vol. 5, Issue 6, 2017.
[17] P. Pareta, M. Rai & M. Gangwar, “An Integrated Approach for effective Intrusion Detection with ElasticSearch”, International Journal of Scientific Research in Computer Sciences and Engineering, Vol. 6, Issue 3, pp.13-17, 2018.

Queen .U. Agunya, N.D. Nwiabu, "Security of Smart Home Intrusion Detection Systems using Data Mining Technique," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.1169-1176, 2019.

Properly tested software is better in quality then the software tested using a poor approach or not tested. Increasing size and complexity of software makes manual testing process a time, cost and resource consuming task. Automating the testing process can improve software development process. The unified modeling language (UML) is the most widely used language to describe the analysis and designs of object-oriented software. Test cases can be derived from UML models more efficiently. In our work, we propose a novel approach for automatic test case generation from the combination of UML state chart, sequence and activity diagrams. In our approach, we first draw the UML state chart, sequence and activity diagrams. Then convert these diagrams to graphs and generate a combined graph. This graph is then used to generate test paths. We have integrated meta-heuristic algorithm i.e. Genetic Algorithm (GA) for this purpose and found fruitful results.

UMLDiagram, Sequence Diagram, Activity Diagram, Test case generation, Genetic Algorithm

[1] Yoo-Min Choi and Dong-Jin Lim. Automatic feasible transition path generation from UML state chart diagrams using grouping genetic algorithms. 94:38–58, 2018.
[2] Rathee N. & Chhillar,” A Survey on Test Case Generation Techniques Using UML Diagrams”, Journal of Software, vol. 12, 8 August 2017.
[3] Khurana N., R.S Chillar,”Test Case Generation and Optimization using UML Models and Genetic Algorithm”, 3rd International Conference on Recent Trends in Computing 2015, Sciencedirect, PP.996-1004.
[4] Akshat Sharma, Rishon Patani, Ashish Aggarwal,”Software Testing Using Genetic Algorithms”, International Journal of Computer Science & Engineering Survey vol.7,No.2, April 2016, PP-21-33.
[5] Itti Hooda, R.S Chillar,”Test Case Optimization and Redundancy Using GA and Neural Networks”, International Journal of Electrical and Computer Engineering vol.8, No.6, December 2018, PP-5449-5456.
[6] Abdelkamel Hettab, Elhillali Kerkouche, Allaoua Chaoui,”A Graph Transformation Approach for Automatic Test Cases Generation from UML activity Diagram”,C3S2E 2015,ACM,2015.
[7] Fernando AugustoDiniz, Glaucia Braga e Silva,”EastTest: An approach for Automaric Test Cases Generation from UML Activity diagram.”, Springer,2018
[8] Meiliana, Irwandhi Septian, Ricky Setiawan Alianto, Daniel, Ford Lumban Gaol,”Automated Test Case Genartaion from UML Activity Diagram and Sequence Diagram using Depth First Search Algorithm”, 2nd International Conference on Computer Science and Computational Intelligence 2017,ICCSCI,ScienceDirect,october2017,PP-629-637.
[9] Monalisa Sharma, Debashish Kundu, Rajib Mall,”Automatic Test Case Generation from UML Sequence Diagrams” the proceeding of IEEE Conference on Software Maintainance,2007,IEEE. PP-996-1004.
[10] Ranjita Kumari Swain, Vikas Panthi, Prafulla Kumar Beher,”Generation of test cases using Activity Diagram” ,ISSN(PRINT):2231-5292, Vol.3,Issue-2,2013
[11] Saso Karakatic, Tina Schweighofer,”A Novel Approach to Generating Test Cases with Genetic Programming”,Springer,2015,PP-260-271.
[12] Ajay Kumar Jena, Santosh Kumar Swain, Durga Prasad Mohapatra,” Test Case Creation from UML Sequence Diagram: A Soft Computing Approach”, Springer, Proceedings of ICCD 2014, Volume 1
[13] Arvinder Kaur, Vidhi Vig,” Automatic test case generation through collaboration diagram: a case study”, Springer, 2017.
[14] S. Dubey, R. Jhaggar, R. Verma, D. Gaur, “Encryption and Decryption of Data by Genetic Algorithm”, International Journal of Scientific Research in Computer Science and Engineering, Vol. 5 No. 3, pp. 42-46, June 2017.
[15] G.R. Shahmohammadi and Kh.Mohammadi, “Key Management in Hierarchical Sensor Networks Using Improved Evolutionary Algorithm”, International Journal of Scientific Research in Network Security and Communication, Vol. 4, No. 2, pp. 5-14, April 2016.

Preeti, Rohit Goyal, "Generating Optimized Test Case from UML Diagram Using Meta-Heuristic Algorithm," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.1177-1183, 2019.

Mining association rules help data owners to unveil hidden patterns from their data to analyze & predict the operation on application domain. However, mining rules in a distributed environment is not a minor task due to privacy concerns. Data owners are interested in collaborating to mine rules on different levels; however, they are concerned that sensitive information related to somebody involved in their database might get compromised during the mining process. Here formulate the problem to solving association rules queries in a environment such that the mining process is confidential and the outcomes are differentially private. Work proposes a privacy-preserving association rules mining where strong association rules are determined privately, and the results returned satisfy differential privacy. Finally done experiments on real-life data it shows that designed approach can efficiently answer association rules queries and is scalable with increasing data records.

Association rules mining, Data Privacy, Data Mining, High confidence

[1] R. Agrawal and R. Srikant. “Privacy preserving data mining”, InProceedings of International Conference on Management of Data (ACMSIGMOD), 2000.
[2] R. Bhaskar, S. Laxman, A. Smith, and A. Thakurta. “Discoveringfrequent patterns in sensitive data”. In Proc. of Intl. Conf. on Knowledge Discovery and Data Mining (KDD), pages 503–512, 2010.
[3] R. Chen, B. C. Fung, B. C. Desai, and N. M. Sossou. “Differentiallyprivate transit data publication: a case study on the Montrealtransportation system”. In Proc. of Intl. Conf. on Knowledge Discoveryand Data Mining (KDD), pages 213–221, 2012.
[4] G. Cormode, C. Procopiuc, E. Shen, D. Srivastava, and T. Yu.”Differentially private spatial decompositions.” In ICDE, pages 20–31, 2012.
[5] C. Dwork, F. McSherry, K. Nissim, and A. Smith. “Calibrating noiseto sensitivity in private data analysis”. In TCC, pages 265–284, 2006.
[6] C. Dwork, M. Naor, O. Reingold, G. N. Rothblum, and S. Vadhan.”On the complexity of differentially private data release: Efficient algorithms and hardness results”. In ACM Symposium on Theory ofComputing, pages 381–390, 2009.
[7] C. Dwork and A. Roth. “The algorithmic foundations of differentialprivacy”. Foundations and Trends in Theoretical Computer Science,9(34):211–407, 2014.
[8] A. Friedman and A. Schuster. “Data mining with differentialprivacy”. In Proc. of Intl. Conf. on Knowledge Discovery and DataMining (KDD), pages 493–502, 2010.
[9] A. Ghosh, T. Roughgarden, and M. Sundararajan. “Universally utility-maximizing privacy mechanisms”. In ACM Symposium onTheory of Computing, pages 351–360, 2009.
[10] Omar Abdel Wahab, Moulay Omar Hachami etal “DARM: A Privacy-preserving Approach for Distributed Association Rules Mining on Horizontally-partitioned Data”. Conference Paper • July 2014 DOI: 10.1145/2628194.2628206
[11] Pradeep Chouksey, "Mining Frequent model Using mass-produced Approach", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.4, pp.89-94, 2017
[12] P.V. Nikam, D.S. Deshpande, "Different Approaches for Frequent Itemset Mining", International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.2, pp.10-14, 2018

Mamata S. Kalas, Amruta G. Unne, "High Confidence Association Rule for Product Selling Strategy," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.1184-1188, 2019.

Text classification is a task of distribution of collection of predefined classes to free-text. Text classifiers are not able to organize, structure, and reason just about something. In this work we have used random forest and naïve Bayes algorithms to perform document classification task. We have trained the machine learning models to inference the respective class of the documents. By working on very big data sets of movie reviews the chosen machine learning models predict whether the reviews are positive or negative and then we analyse and compare the results of each model’s individual confusion matrix like precision, recall, f1-score & support. An important observation is that for the same input data random forest provides more relevant results as compared to naïve bayes algorithm. But as the training data grows naïve bayes also performs equally good as random forest.

Text Classification, Naïve Bayes, Random Forest, Machine Learning

[1] Agarwal, B. Xie, I. Vovsha, O. Rambow, and R.Passonneau, “Sentiment Analysis of Twitter Data,” Annual International Conference New York: Columbia University, 2012.
[2] M.Rambocas, and J. Gama, “Marketing Research: The Role of Sentiment Analysis”. The 5th SNA-KDD Workshop’11. University of Porto, 2013.
[3] Andrew Mc Callumzy, and Kamal Nigamy. “A Comparison of Event Models for Naive Bayes Text Classification”. Learning for Text Categorization: Papers from the 1998 AAAI Workshop, pp. 41-48.
[4] Zu G., Ohyama W., Wakabayashi T., Kimura F., "Accuracy improvement of automatic text classification based on feature transformation": Proc: the 2003 ACM Symposium on Document Engineering, November 20-22, 2003, pp.118-120
[5] Chaudhary, A., Kolhe, S., Kamal, R., 2016. A hybrid ensemble for classification in multiclass datasets: An application to oilseed disease dataset. Computers and Electronics in Agriculture 124, pp.65–72.
[6] Chaudhary, A., Kolhe, S., Kamal, R., 2016. An improved random forest classifier for multi-class classification. Information Processing in Agriculture 3, pp. 215-222.
[7] Chaudhary, A., Kolhe, S., Kamal, R., 2012. Machine learning techniques for mobile intelligent systems: A study. In IEEE Ninth International Conference on Wireless and Optical Communications Networks (WOCN), pp. 1-55.
[8] Chaudhary, A., Kolhe, S., Kamal, R., 2013. Machine Learning Classification Techniques: A Comparative Study. International Journal on Advanced Computer Theory and Engineering 2(4), pp. 21-25.
[9] Chaudhary, A., Kolhe, S., Kamal, R., 2013. Machine Learning Techniques for Mobile Devices: A Review. International Journal of Engineering Research and Applications 3(6), pp. 913-917.
[10] Chaudhary, A., Kolhe, S., Kamal, R., 2013. Performance Examination of Feature Selection methods with Machine learning classifiers on mobile devices. International Journal of Engineering Research and Applications 3(6), pp.587-594.
[11] Thakur, A., Thakur, R., 2018. Machine Learning Algorithms for Intelligent Mobile Systems. International Journal of Computer Sciences and Engineering 6(6), pp. 1257-1261.
[12] http://www.cs.cornell.edu/people/pabo/movie-review-data/poldata.README.2.0.txt
[13] https://www.anaconda.com/distribution/#download-section
[14] https://stackabuse.com/using-regex-for-text-manipulation-in-python/
[15] A. Pak, and P. Paroubek, “Twitter as a Corpus for Sentiment Analysis and Opinion Mining,” Special Issue of International Journal of Computer Application, France: Universitede Paris-Sud, 2010.
[16] Forman, G., 2003. “An Experimental Study of Feature Selection Metrics for Text Categorization”. Journal of Machine Learning Research, 3 2003, pp. 1289-1305
[17] https://towardsdatascience.com/machine-learning-nlp-text-classification-using-scikit-learn-python-and-nltk-c52b92a7c73a
[18] Y.H.LI and A.K Jain “Classification of text document”, the computer Journal, vol.41, pp. 8,1998
[19] https://monkeylearn.com/text-classification/
[20] https://www.analyticsvidhya.com/blog/2018/04/a-comprehensive-guide-to-understand-and-implement-text-classification-in-python/

Rahul Jain, Archana Thakur, "Comparative Study of Machine Learning Algorithms for Document Classification," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.1189-1191, 2019.

In this paper the concept of fuzzy labeling of fan graph has been introduced, the generalized formula for n- dimensional fan of the vertices and edges and the new algorithm of fuzzy labelling of fan graph have been discussed. Every membership function of fuzzy labeling of a fan graph must be distinct.

fuzzy path , fuzzy graph, fuzzy labeling graph, fan graph

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[3] A.Rosenfeld, Fuzzy Graph, In: L.A. Za-deh, K.S. Fu and M.Shimura, Editors, Fuzzy Sets And their Applications to cogni-tive and decision Process, Academic press, New York (1975) 77-95
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[5] A Nagoor Gani D. Rajalaxmi (a) Subahashini Metric in Fuzzy Labeling Graph Interna-tional journal of Fuzzy Mathematical Ar-chive Vol. 5, No. 2, 2014, 113-122.
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M.K. Pandurangan, T. Bharathi, S.Antony Vinoth, "Fuzzy Labeling on Cycle Related Graph," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.1192-1194, 2019.

The detection of outliers in high-dimensional data raises some of the challenges of “dimension curse”. A major point of view is that the concentration of distances, that is, the distance trends in high-dimensional data becomes illegible, making it difficult to detect outliers by marking all points as values by a distance-based approach. In this paper, implement that the idea of distance-based methods can produce more contrast outliers in high-dimensional environments to provide evidence to support the idea that this view is too simple. In addition, we show that high dimensions can have different effects when there is no oversight to re-examine the concept of a more recent inverse neighbor in the context of atypical detection. It has recently been observed that the distribution of the inverse neighborhood count of points deviates in a high dimension, which causes a phenomenon called a hubness. This work provide information on how some antihubs rarely appear in the k-NN list at other points, and explain the connection between antihubs, outlier values and existing unsupervised outlier detection methods. In evaluating the classical approach to k-NN, angle-based techniques are designed for high-dimensional data, local outliers based on density, and various methods based on anti-sheathing. Combining and real-world data, this work provide new information about the utility of reverse neighborhood counting to detect outliers without supervision.

Clustering, data mining, fuzzy c-means, outliers, unsupervised learning

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[5]. Unsupervised Distance-Based Outlier Detection Using Nearest Neighbours Algorithm on Distributed Approach: Survey by Jayshree S.Gosavi , Vinod S.Wadne, (An ISO 3297: 2007 Certified Organization) IJIRCCE ,Vol. 2, Issue 12, December 2014
[6]. Pamula, Rajendra, Jatindra Kumar Deka, and Sukumar Nandi. "An outlier detection method based on clustering." Emerging Applications of Information Technology (EAIT), 2011 Second International Conference on. IEEE, 2011.
[7]. W. Jin, A. K. H. Tung, and J. Han. Finding top-n local outliers in large database. In 7th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pages 293–298, 2001.
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S. Vasuki, "Unsupervised Distance-Based Outlier Detection using Reversible KNN with Fuzzy Clustering," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.1195-1199, 2019.

For proper planning and operation of power system and economic scheduling of generating units, power flow analysis plays a vital role. It is performed to have clear knowledge regarding bus voltage magnitude and angle and line flows. A number of methods are being used all over the world for power flow analysis. Newton Raphson method, Gauss Seidal method, fast decoupled load flow methods are a few to name. Now days, various soft computing techniques are adopted by researchers as well as practicing engineers for load flow analysis to cater various needs of the research institutes and the utilities. Every method has got advantages as well as disadvantages. The objective of this paper is to develop an user friendly software to perform load flow analysis for IEEE 14 bus system. Optimization of IEEE 14 bus sytem has been performed using Particle Swarm Optimization Technique. The software will be helpful for researchers, practicing engineers, students of power system of various levels to carry out power flow quickly and efficiently as per their requirement. The software is developed using MATLAB programming.

14 bus system, power flow, optimization

[1] N. Usha, “Simulation results of eight bus system using pushpull inverter base STATCOM”, Journal of Theoretical and Applied Information Technology, 2005 - 2009 JATIT.
[2] C. R. Feurte – Esquivel and E. Acha `A Newton-type algorithm for the control of power flow in electrical power networks," IEEE Transactions on Power Systems, Vol.12,
Nov.1997.
[3] Rohit Kapahi, “Load flow analysis of 132 KV substation using ETAP software”, International Journal of Scientific & Engineering Research Volume 4, Issue 2, February- 2013,ISSN 2229-5518
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[5] D.I.Sun, B.Ashley, B.Brewer, A.Hughes and W.F.Tinney, “Optimal Power Flow by Newton Approach”, IEEE Transactions on Power Apparatus and systems, vol.103, No.10, 1984, pp2864-2880.
[6] Mr. Rudresh. B. Magadum, Mr. Tejaswi. M. Timsani, “Minimization of Power Loss in Distribution Networks by Different Technology”, International Journal of Scientific & Engineering Research Volume 3, Issue 5, 2012
[7] P. V. V. Rama Rao and S. Sivanagaraju, “Radial istribution NetworkReconfiguration for Loss Reduction and Load Balancing using Plant Growth Simulation Algorithm”, International Journal on Electrical Engineering and Informatics - Volume 2, Number 4, 2010
[8] Ajinkya Pachghare , R.M.Sahare, “Voltage regulation and reactive power compensation by SSSC based on 48-pluse GTO (VSC)”, ISROSET, Vol.5, Issue.4, pp.41-44, August (2017)
[9] D.D. Bhale, R.B. Jain, “Performance Analysis of Hybrid Cognitive Gaussian Relay Channels”, IJSRNSC, Volume-5, Issue-2, May 2017

Sanjib Hazarika, "Optimized Power Flow Analysis of IEEE 14 Bus System Using Matlab," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.1200-1203, 2019.

C is a programming language which is the most powerful and useful language ever for the programmers and developers. Like all the modern programming languages, the C language also has many control statements out of which five are iterative statements, i.e., while, do-while and for. These three statements are meant for use in same conditions, but which one is better. The performance of these three statement does not compared by the novice programmers. So, a basic knowledge of performance of these loops should be there. This comparison will guide novice programmers to use these loops efficiently. The comparison can be done by counting the execution time but that can depend on other factors also like CPU usage by other programs or services etc. But a very efficient way to compare is the comparison of Assembly Instruction generated by a program, so here we are presenting a comparison based on the assembly code generation by each loop which can be seen by the object file created just after the compilation.

while, do-while, for, assembly code

[1]. Mark Burgess, “The GNU C Programming Tutorial”, Ron Hale-Evans, Norway, pp. 61-68, 2002
[2]. E Balagurusamy, “Programming in ANSI C”, Tata McGraw-Hill, India, pp 154-159, 2007
[3]. Joseph Cavanagh, “X86 Assembly Language and C Fundamentals”, CRC Press Taylor & Francis Group, New York, pp 251-266, 2013

J. Makhijani, M. Niranjan, Y. Sharma, "A Comparison of while, do-while and for loop in C programming language based on Assembly Code Generation," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.1204-1211, 2019.