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In the current scenario there are different types of research has been performed in the field of text detection and extraction from images. Different types of processes used different types of application of the text extraction from the images. With the help of the Text Extraction process we can easily find important data from images. There are various types of Image processing techniques have been evolved for Text Extraction from the image scene and videos. Every process has different types of factors like Precision, Speed, Complexity and Time required, etc. but each process gives different result in each field. Some process has advantage and disadvantage related to these factors, so we can say single process is inadequate for the complete process of text detection and extraction. For the preferable performance, we present the combine form of different processes. This paper contains combination of two different processes for text detection and extraction from video surveillance system just like processing of images from image scenes.

Text detection, Text extraction, Image processing, Precision, Preferable, Video surveillance

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Namrata Choudhary , Kirti Jain, "Review on Text Detection and Extraction for Catching Identities of Objects in Road Video Scenes," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.486-490, 2018.

Search-Based Software Testing (SBST) is a form of Search-Based Software Engineering (SBSE) to optimize testing through the use of computational search. Search Based Software Testing (SBST) denotes process of meta-heuristics for optimization of task into perspective of software testing. The expenditure of meta-heuristics analysis activities are accomplished for upper numbers of inputs that essential to be verified. In this paper we explain how we created an efficient testing techniques to compare searches based Meta heuristics algorithms based on their parameter and get their performance and test them.These Algorithms are used to generate automatic test data that satisfy branch coverage, path coverage, cost and time and quality in software testing. Cost of testing behavior has primary section of the total cost of software. Finally, this paper present the result which carried out to estimate the efficiency of the proposed techniques with new fitness function compared to each other based on their parameters and analyses the performance. SBST for test Generation, efficient Meta-Heuristics search Algorithms.

Meta heuristics Algorithms, Search Based Software Engineering, Software Quality,Path coverage, Branch coverage, Software Testing, Automated Test Case Generation

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Mandeep Kumar, Deepak Nandal, "A Review on Analysis Search Based Software Testing using Metaheuristics Techniques," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.491-497, 2018.

ERASMUS is a chat bot on Facebook which is used to answer the queries related to the college information in one go. There has been an increasing demand for Chatbots on the websites of colleges or Educational Institutes. The college websites are generally unmaintained and filled with redundant information. Finding the required information on the website becomes a tedious task for students, teachers, parents or other users. The problem also gains a boost during time of admission, as the requirement for information increases during that time. A chatbot on the college website will help the users find the required information in a few clicks and questions. The chat bot detects the intent of the queries received from the users and accordingly parses the website to find the related information. There is no specific format the user must follow. The system uses built in artificial intelligence to answer the queries. The user can query any college related activities through the system. The user does not have to personally go to the college website for any enquiry.

Chatbot, Intent, API.AI, AI, Natural Language Processing, Machine Learning, Artificial Intelligence

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[6] Bonnie Chantarotwong, “The Learning Chatbot Bonnie”, IMS-256 Final Project, Fall 2006
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[8] Shah, Rishabh, Siddhant Lahoti, and K. Lavanya. "An intelligent chat-bot using natural language processing." International Journal of Engineering Research 6.5 : 281-286. 2017.

Jash Thakkar, Purva Raut, Yash Doshi, Krishma Parekh, "Erasmus – AI Chatbot," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.498-502, 2018.

The size of data generated every second has crossing the boundary of usual data size as a result of the rapid growth and spread of communication technology. This notable increase is of great importance and has gained the scholars’ interest. In other words, the increasing of data make the current ear an era of big data. Nowadays, one of the vital challenges is to get big data secured. Cryptography is an important technique that provides a high data security in many environments and applications. Homomorphic Encryption (HE), a special direction of cryptography, can address such security issues in big data environment. This paper concerns with the HE schemes which can play a vital role in securing big data environment. Therefore, big data concepts and characteristics are reviewed in the current paper along with full description of HE schemes, covering the HE types and illustrating their mechanisms for securing big data. In addition, the current paper offers some interpretations on the base of some security features along with big data security model.

Big Data, Big Data Security, Homomorphic Encryption, PHE, SWHE, FHE

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Galal A. AL-Rummana, G. N. Shende, "Homomorphic Encryption for Big Data Security: A Survey," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.503-511, 2018.

It is Impossible to build a software which is completely tested or bug free. Manual bug fixing is very time taking, costly and clumsy task. To automate the process of software bug fixing various machine learning techniques are employed. Software bug prediction is implemented before testing phase of software development life cycle model while bug handling is a post testing phase arises after the failure of test cases. Software bug handling deals with the phases of software bug life cycle model. Bug reports are one of the most important software artifacts for handling of bugs. In recent years, due to release of thousands of open source software, large amount of repositories (like bug repositories) are available for software analytics. Analytics help software practitioners in taking decisions with logic instead of intuitions which make it more accurate and practical. Prediction and Handling of software bugs uses this analytics in automation with the help of machine learning techniques. In this paper we focused on predictive capability of different machine learning techniques in association with software bug prediction and handling. Findings and previous work is summarized with the help of tables (in association with attributes) and diagrams (in mapping with software bug life cycle model).

Software Bug, Computational Intelligence, Analytics, Naïve Bayes (NB), Support Vector Machine (SVM)

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[15] Zimmermann, T., Premraj, R., Bettenburg, N., Just, S., Schroter, A. and Weiss, C., 2010. What makes a good bug report?. IEEE Transactions on Software Engineering, 36(5), pp.618-643.
[16] Bachmann, A. and Bernstein, A., 2010, May. When process data quality affects the number of bugs: Correlations in software engineering datasets. In Mining Software Repositories (MSR), 2010 7th IEEE Working Conference on(pp. 62-71). IEEE.
[17] Xia, X., Lo, D., Ding, Y., Al-Kofahi, J.M., Nguyen, T.N. and Wang, X., 2017. Improving automated bug triaging with specialized topic model. IEEE Transactions on Software Engineering, 43(3), pp.272-297.
[18] Kumar, R. and Gupta, D.L., 2016. Software Bug Prediction System Using Neural Network. European Journal of Advances in Engineering and Technology, 3(7), pp.78-84.
[19] Tian, Y., Lo, D. and Sun, C., 2012, October. Information retrieval based nearest neighbor classification for fine-grained bug severity prediction. In Reverse Engineering (WCRE), 2012 19th Working Conference on (pp. 215-224). IEEE.
[20] Lamkanfi, A., Demeyer, S., Giger, E. and Goethals, B., 2010, May. Predicting the severity of a reported bug. In Mining Software Repositories (MSR), 2010 7th IEEE Working Conference on (pp. 1-10). IEEE.
[21] Xuan, J., Jiang, H., Ren, Z. and Zou, W., 2012, June. Developer prioritization in bug repositories.
In Software Engineering (ICSE), 2012 34th International Conference on(pp. 25-35). IEEE.
[22] Jain, S. and Wilson, S.R., 2016, August. Automated bug assortment system in datasets. In Inventive Computation Technologies (ICICT), International Conference on (Vol. 2, pp. 1-7). IEEE.
[23] D`Ambros, M., Lanza, M. and Robbes, R., 2010, May. An extensive comparison of bug prediction approaches. In Mining Software Repositories (MSR), 2010 7th IEEE Working Conference on (pp. 31-41). IEEE.
[24] Ayari, K., Meshkinfam, P., Antoniol, G. and Di Penta, M., 2007, October. Threats on building models from cvs and bugzilla repositories: the mozilla case study. In Proceedings of the 2007 conference of the center for advanced studies on Collaborative research (pp. 215-Yang, X.L., Lo, D., Xia, X., Huang, Q. and Sun, J.L., 2017. High-impact bug report identification with imbalanced learning strategies. Journal of Computer Science and Technology, 32(1), pp.181-198.228). IBM Corp..
[25] Yang, X.L., Lo, D., Xia, X., Huang, Q. and Sun, J.L., 2017. High-impact bug report identification with imbalanced learning strategies. Journal of Computer Science and Technology, 32(1), pp.181-198.
[26] Anbalagan, P. and Vouk, M., 2009, September. On predicting the time taken to correct bug reports in open source projects. In Software Maintenance, 2009. ICSM 2009. IEEE International Conference on (pp. 523-526). IEEE.
[27] Anvik, J., Hiew, L. and Murphy, G.C., 2006, May. Who should fix this bug?. In Proceedings of the 28th international conference on Software engineering (pp. 361-370). ACM.
[28] Puranik, S., Deshpande, P. and Chandrasekaran, K., 2016. A Novel Machine Learning Approach for Bug Prediction. Procedia Computer Science, 93, pp.924-930.
[29] Arudkar, S. and Pimpalkar, A., Design of an Effective Mechanism For Automated Bug Triage System.
[30] Singla, H., Sharma, G. and Sharma, S., 2016. Domain Specific Automated Triaging System for Bug Classification. Indian Journal of Science and Technology, 9(33).
[31] Singh, V.B., Misra, S. and Sharma, M., 2017. Bug Severity Assessment in Cross Project Context and Identifying Training Candidates. Journal of Information & Knowledge Management, 16(01), p.1750005.
[32] Chaturvedi, K.K. and Singh, V.B., 2012. An empirical comparison of machine learning techniques in predicting the bug severity of open and closed source projects. International Journal of Open Source Software and Processes (IJOSSP), 4(2), pp.32
[33] Sharma, M., Kumari, M. and Singh, V.B., 2015, June. Bug assignee prediction using association rule mining. In International Conference on Computational Science and Its Applications (pp. 444-457). Springer, Cham.
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[35] Di Nucci, D., Palomba, F., De Rosa, G., Bavota, G., Oliveto, R. and De Lucia, A., 2018. A developer centered bug prediction model. IEEE Transactions on Software Engineering, 44(1), pp.5-24.
[36] Anvik, J. and Murphy, G.C., 2007, May. Determining implementation expertise from bug reports. In Proceedings of the Fourth International Workshop on Mining Software Repositories (p. 2). IEEE Computer Society.
[37] Osman, H., Ghafari, M. and Nierstrasz, O., 2017, February. Automatic feature selection by regularization to improve bug prediction accuracy. In Machine Learning Techniques for Software Quality Evaluation IEEE Workshop on (pp. 27-32).
[38] Shivaji, S., Whitehead, J.E.J., Akella, R. and Kim, S., 2009, November. Reducing features to improve bug prediction. In Proceedings of the 2009 IEEE/ACM International Conference on Automated Software Engineering (pp. 600-604).
[39] Giger, E., Pinzger, M. and Gall, H., 2011, September. Using the gini coefficient for bug prediction in eclipse. In Proceedings of the 12th International Workshop on Principles of Software Evolution (pp. 51-55).
[40] Xia, X., Lo, D., Shihab, E., Wang, X. and Zhou, B., 2015. Automatic, high accuracy prediction of reopened bugs. Automated Software Engineering, 22(1), pp.75-109.
[41] Xia, X., Lo, D., Wang, X., Yang, X., Li, S. and Sun, J., 2013, March. A comparative study of supervised learning algorithms for re-opened bug prediction. In Software Maintenance and Reengineering (CSMR), 2013 17th European Conference on(pp. 331-334).

Tamanna, Om Prakash Sangwan, "Software Bug Prediction and Handling Using Machine Learning Techniques: A Review," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.512-517, 2018.

Mobile Ad-hoc network (MANET) is a type of ad hoc network that can change locations and configure itself on the fly and wireless communications. The mobile nodes communicate with each node without any centralized manner. The MANETs does not have any pre-defined network topology. The MANET node wants to send the data between two nodes to establish wireless connection to exchange the data. After data sending is finished the connection is ended. The mobile network topology is regularly changing the location due to nodes moving, resource limitation and bandwidth limitation of mobile node. There are many types of routing protocol available in MANET. In this paper, we discuss about different type of existing MANET routing protocols and its security problems. The MANET routing protocols offered for secure packets sending and face some common attack in ad hoc networks. The routing protocol are subjected to situation against the most frequently identified attack sink hole, grey hole, spoofing, black hole attack and wormhole attack etc. A survey of various issues in MANET regarding security is done considering all major aspects.

Mobile Ad-hoc Network, Security Issues, Routing Protocols, Attacks

[1] Ali Dorri “An EDRI-based approach for detecting and eliminating cooperative black hole nodes in MANET”, Springer Science Business Media New York 2016.
[2] Gayathri Dhananjayan and Janakiraman Subbiah, “T2AR: trust‑aware ad‑hoc routing protocol for MANET”, Springer Plus (2016) 5:995.
[3] Nadav Schweitzer, Ariel Stulman, Member, Asaf Shabtai and Roy David Margalit “Contradiction Based Gray-Hole Attack Minimization for Ad-Hoc Networks”, in: IEEE Transactions on Mobile Computing, Volume: 16 Issue: 8, 2017.
[4] Neelam Janak Kumar Patel, Dr. Khushboo Tripathi, “Trust Value based Algorithm to Identify and Defense Gray-Hole and Black-Hole attack present in MANET using Clustering Method”, IJSRSET, Volume 4, Issue 4, 2018.
[5] R C Poonia, D. Bhargava, and B.Suresh Kumar. “CDRA: Cluster-based dynamic routing approach as a development of the AODV in vehicular ad-hoc networks”, In Signal Processing and Communication Engineering Systems (SPACES), 2015 International Conferenceon, pp.397-401. IEEE, 2015.
[6] S. Hazra, and S.K. Setua. “Black Hole Attack Defending Trusted On-Demand Routing in Ad-Hoc Network”, In Advanced Computing, Networking and Informatics-Volume 2, pp.59-66. Springer International Publishing, 2014.
[7] S. Jain, M. Jain, H. Kandwal, “Advanced Algorithm for Detection and Prevention of Cooperative Black and Gray hole Attacks in Mobile Ad-hoc Networks”, Intl. Journal of Computer Application 1(7): 37-42, Feb. 2010.
[8] A.Siddiqua, S.Kotari, and AAKhan Mohammed. “Preventing black hole attacks in MANETs using secure knowledge algorithm”, Signal Processing and Communication Engineering Systems (SPACES), 2015 International Conferenceon. IEEE, 2015.
[9] N.Choudhary and L.Tharani. “Preventing black Hole attack in AODV using timer-based detection mechanism”, Signal processing and communication engineering systems (SPACES), 015 international conferenceon. IEEE,2015.
[10] AK Jain and V Tokekar. “Mitigating the effects of Black hole attacks on AODV routing protocol in Mobile adhoc Networks”, Pervasive computing (ICPC), 2015 international conference on. IEEE, 2015.

K. Sumathi, D. Vimal Kumar, "A Survey of Routing Protocols and Security Issues in MANET," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.518-523, 2018.

In most of the applications in today’s world, data is fetched from the secondary storage i.e. hard disk. The user connects directly to the database for fetching the data. In current approach of cache management and loading, data is loaded from the secondary storage i.e. from the database to the primary storage i.e. cache. In case of any cache failure, entire data has to be reloaded from the database which consumes a lot of time in case the volume of data runs into millions. In the proposed design, data will be fetched from cached data and will be displayed to the end user. In this design of the cache reload we will be persisting the cached objects in some persistent storage as jms topics or flat files. The cache will be rebuilt again from these objects rather than from the database in case of any cache failures. In addition to it, analysis and performance of cache has been shown in this paper considering various parameters.

Coherence cache, hard drive, secondary storage

[1] Kuber Vikram Singh and Inderjeet Yadav, “Improving Data Access Performance Using Coherence Caching in SOA and ADF Application." International Journal on Recent and Innovation Trends in Computing and Communication ISSN: 2321-8169 Volume: 3 Issue: 5 (2015)
[2] Tamura, E., et al. "High performance memory architectures with dynamic locking cache for real-time systems." Proceedings of the 16th Euromicro Conference on Real-Time Systems, Italy. 2004.
[3] Chaplot, Vivek. "Cache Memory: An Analysis on Performance Issues." International Journal 4, no. 7 (2016).
[4] Mathew, Sharon, and D. M. Jagadeeswari. "Task scheduling and memory partitioning for multiprocessor system-on-chip using low-power L2 cache architecture." Int J Emerg Trends Eng Dev 2, no. 3 (2013): 95-105.
[5] Patel, Ronak. "Implementation of Cache Designs and Efficiency of Cache Memory."
[6] Dimple Juneja and Atul Garg, “Collective Intelligence based framework for load balancing of Web servers”, IJICT, Vol 3 No-1 Jan-2012
[7] Sandeep Sharma, S.Singh and Meenakshi, “Performance analysis of load balancing algorithms”, World academy of science, 2008
[8] Jospeh Kee Yin Ng and Chui Ying Hui, “ Client-Side caching strategies and On demand broadcast Algorithms for Real Time Information”, IEEE , March 2008.
[9] Hossam Hassanein, Zhengang Liang and Patrick Martin, “Performance Comparison of Alternative Web Caching Techniques”, Procceedings of the seventh International Symposium on Computers and Communications, IEEE, 2002.
[10] M. Liu, F. Wang, D. Zeng and L.Yang, “An Overview of world wide Web Caching”, International conference on Systems Man and Cybernetics, IEEE, 2001, pp.3045-3050.

Ankush Sharma, Vishal Gupta, "Analysis and Performance of Cache Using Persisted Java Topics," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.524-529, 2018.

Requirement Engineering is a very important phase in SDLC. The success and failure of the end product has direct connection with this Requirement Phase. So the output quality of this phase plays a vital role. Requirement Prioritization Process (one of the process) in this phase helps the engineers to work out and find the prioritization among the requirements. Because of the constraints – cost, time and other factors, prioritization plays an imperative role in the development of project and also to improve the goodwill of the company in the competitive market. In this paper, the Requirement Prioritization techniques are discussed and research articles related to the topics are reviewed. Based on the analysis of previous research, the comparisons between the mostly used models are made, drawbacks and strengths are discussed.

Requirement Prioritization, AHP, Prioritization techniques

[1]. Kanos Matyokurehwa, Nehemiah Mavetera, Osden Jokonya, Requirements Engineering Techniques: A Systematic Literature Review, International Journal of Soft Computing and Engineering (IJSCE), ISSN: 2231-2307, Volume-7 Issue-1, March 2017.
[2]. Süleyman Kıvanç Ekici1, Ahmet Oturgan1, Deniz Kılınç2, Ceyhun Araz3, Deniz Kilinç , Software Requirements Prioritization:A Case Study ,Conference: UBMK 2016 (Uluslararası Bilgisayar Bilimleri ve Mühendisliği Konferansı, International Conference on Computer Science and Engineering), At Tekirdağ .
[3]. Javed Ali Khan, Izaz Ur Rehman, Yawar Hayat Khan, Iftikhar Javed Khan, Salman Rashid, I.J., Comparison of Requirement Prioritization Techniques to Find Best Prioritization Technique, Modern Education and Computer Science, 2015, 11, 53-59 Published Online November 2015 in MECS (http://www.mecs-press.org/) DOI: 10.5815/ijmecs.2015.11.06,
[4]. Manju Khari*1, Nikunj Kumar2, Comparison of six prioritization techniques for software requirements,Volume 4, No. 1, January 2013, Journal of Global Research in Computer Science, 4 (1), January 2013, 38-43
[5]. Aneesa Rida Asghar, Atika Tabassum, Dr. Shahid Nazir Bhatti, Dr. S Asim Ali Shah, The Impact of Analytical Assessment of Requirements Prioritization Models: An Empirical Study, International Journal of Advanced Computer Science and Applications • February 2017 DOI: 10.14569/IJACSA.2017.080240.
[6]. Mulugu.Narendhar 1, Dr.K.Anuradha2, Different Approaches of Software Requirement Prioritization, International Journal of Engineering Science Invention ISSN (Online): 2319 – 6734, ISSN (Print): 2319 – 6726 www.ijesi.org ||Volume 5 Issue 9|| September 2016 || PP. 38-43.
[7]. Anuja soni, An Evaluation of Requirements Prioritisation Methods, International Journal of Innovative Research in Advanced Engineering (IJIRAE) ISSN: 2349-2163 Volume 1 Issue 10 (November 2014).
[8]. Hadeel E. Elsherbeiny, 2A. A. Abd El-Aziz, 2Nagy Ramadan, Decision Support for Requirements Prioritization Using Data Analysis, Egyptian Computer Science Journal (ISSN-1110-2586) Volume 41– Issue 2, May 2017.
[9]. Mohammad Alkandari*, Asma Al-Shammeri, Enhancing the Process of Requirements Prioritization in Agile Software Development , A Proposed Model, Journal of Software, Volume 12, Number 6, June 2017, doi: 10.17706/jsw.12.6.439-453.
[10]. Syed Ali Asif1, Zarif Masud1, Rubaida Easmin2, and Alim UlGias1, SAFFRON: A Semi-Automated Framework For Software Requirements Prioritization, arxiv:1801.00354v1 [cs.SE] 31 Dec 2017.
[11]. JOACHIM KARLSSON, KEVIN RYAN, . A Cost–Value Approach for Prioritizing Requirements, Norges Teknisk-Naturvitenskapelige Universitet. Downloaded on August 19, 2009 at 06:20 from IEEE Xplore., 7 4 0 - 7 4 5 9 / 9 7 / $ 1 0 . 0 0 © 1 9 9 7 I E E E
[12]. Nasir mehmood minhas, asif majeed, An integrated approach to requirement Prioritization with reference to Distributed software development, 1st ieee international conference on engineering management 2012, nust pakistan, march 20 – 21, 2012.
[13]. Samina Saghir, Tasleem Mustafa, Requirements Prioritization Techniques for Global Software Engineering , Pak. J. Eng. Technol. Sci. Volume 6, No 1, 2016, pg 42- 63,ISSN: 2222-9930 print, ISSN: 2224-2333 online,
[14]. Nafisa Osman, Abd-El-Kader Sahraoui. A Software Requirement Engineering Framework to Enhance Critical Success Factors for ERP Implementation, International Journal of Computer Applications, Foundation of Computer Science, 2018, 180 (10), pp.32-37. <10.5120/ijca2018916170>.
[15]. Khurram Ejaz, Ayesha Amjad, Model and Technique over SoftwareRequirement Prioritization, Pakistan Journal of Engineering Technology and Science (PJETS) Volume 6, No 2, December 2016.
[16]. Mona Batra, Dr. Archana Bhatnagar, Requirements Prioritization: A Review, International Journal of Advanced Research in Science,Engineering and Technology Vol. 3, Issue 11 , November 2016.
[17]. Syed Zeeshan Hussain.et.al., Selection of Software Requirements using Decision Tree during Software Development Process, Int. Journal of Engineering Research and Application www.ijera.com ISSN: 2248-9622, Vol. 7, Issue 9, (Part -7) September 2017, pp.71-77.
[18]. ANUJA SONI, An Evaluation of Requirements Prioritisation Methods, International Journal of Innovative Research in Advanced Engineering (IJIRAE) ISSN: 2349-2163 Volume 1 Issue 10 (November 2014).
[19]. Dr Zahi Abu-Sarhan, Application Of Analytic Hierarchy Process (AHP) In The Evaluation and Selection Of an Information System Reengineering Projects, IJCSNS International Journal of Computer Science and Network Security, VOL.11 No.1, January 2011.
[20]. Tschangho John Kim, Modified analytic hierarchy process for project Proposal evaluation: An alternative method for Practical implementation, Reg Sci Policy Pract. 2018;10:25–35. Wileyonlinelibrary. com/journal/rsp3.
[21]. Hamed Taherdoost, Decision Making Using the Analytic Hierarchy Process (AHP); A Step by Step Approach, International Journal of Economics and Management Systems http://www.iaras.org/iaras/journals/ijems.

K. Glory Vijayaselvi, Thirumalai Selvi R., "Literature Review on Requirement Prioritization Methods," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.530-535, 2018.

The number of methods available in data mining to detect the outlier by making the clusters of data and then detect the outlier from them. The objects that are similar to each other are organized in group it’s called cluster and the objects that do not comply with the model or general behavior of the data these data objects called outliers. Outliers detect by clustering. Density based clustering algorithm (DENCLUE) is one of the primary methods for clustering in data mining which groups neighboring objects into clusters based on local density conditions rather than proximity between objects. Data points are assigned to a cluster by hill climbing, points going to the same local maximum are put into the same cluster. The traditional density estimation only considers the location of the point, not variable of interest. Depending on the convergence criteria, the method needs less iteration as fixed step size methods and improving cluster quality and also finding an outlier correctly.

Clustering, Data Mining, Density Based Clustering Algorithm, DBSCAN, OPTICS, Outlier Mining

[1]. Harsh Shah, Karan Napanda and Lynette D’mello, “Density Based Clustering algorithm”, International Journal of Computer Engineering, vol. 3,Issue. 11, pp.54-57, Nov 2015, E-ISSN: 2347-2693
[2]. Anoop Kumar Jain, Prof.Satyam Maheswari,“Survey of Recent Clustering Techniques in Data Mining”, International Journal of Computer Science and Management Research ,Vol. 1, Issue 1, Aug 2012.
[3]. R.J Bolton, D.J.Hand, ”Statistical fraud detection”: A review with discussion, Statistical Science, 17(3): pp. 235-255, 2002.
[4]. E. Eskin , A.Arnold , M.Prerau , L.Portnoy , S.Stolfo ,” A geometric framework for unsupervised anomaly detection: Detecting intrusions in unlabeled data”, In Data Mining for Security Applications, 2002.
[5]. E. Knorr, R.Ng., V. Tucakov, ,” Distance-based outliers: Algorithms and applications”, The International Journal on Very Large Data Bases Journal 8, pp. 237–253, 2000.
[6]. J.Laurikkala, M. Juhola and E. Kentala, “Informal Identification of Outliers in Medical Data”, in Fifth International Workshop on Intelligent Data Analysis in Medicine and Pharmacology IDAMAP-2000 Berlin,Organized as a workshop of the 14th European Conference on Artificial Intelligence ECAI-2000.
[7]. M.Breunig, H.Kriegel,R. Ng , J.Sander, “LOF: Identifying density based local outliers”, In: Proc. SIGMOD Conf, pp. 93–104, 2000.
[8]. C.Aggarwal,P.S. Yu , “Outlier Detection for High Dimensional Data”. In: Proceedings of the ACM SIGMOD Conference 2001.
[9]. Anant Ram, Sunita Jalal, S. Anand . Jalal, Manoj kumar, “A density Based Algorithm for Discovery Density Varied cluster in Large spatial Databases”, International Journal of Computer Application Vol. 3,No.6, June 2010.
[10]. R.Prabahari ,V.Thiagarasu , “A Comparative Analysis of Density Based Clustering Techniques for Outlier Mining”, International Journal Of Engineering Sciences & Research Technology, ISSN 2277-9655, pp 132-136 November, 2014.
[11]. Alexander Hinneburg, Daniel A.Keim (1998),"An Efficient Approach to Clustering in Large Multimedia Databases with Noise [Online] Available: http://www.aaai.org
[12]. B.G Obula Reddy, Dr. Maligela Ussenaiah,“Literature Survey On Clustering Techniques”, IOSR Journal of Computer Engineering, Vol. 3, Issue 1, July 2012.
[13]. Mariam Rehman, Syed Atif Mehdi,“Comparison of Density Based Clustering Algorithms”, research work, Lahore College for Women University, Lahore, Pakistan.
[14]. Henrik Bäcklund, Anders Hedblom, Niklas Neijman, “DBSCAN A Density-Based Spatial Clustering of Application with Noise”, 2011.
[15]. Anoop Kumar Jain, Prof.Satyam Maheswari,“Survey of Recent Clustering Techniques in Data Mining”, International Journal of Computer Science and Management Research ,Vol. 1, Issue 1, Aug 2012.
[16]. Rui Xu, Donald Wunsch,“Survey of Clustering Algorithms”, IEEE Transactions On Neural Netwoks, Vol. 16, No. 3, May 2005
[17]. R.Prabahari ,V. Thiagarasu , “Density Based Clustering Using Gaussian Estimation Technique” , International Journal on Recent and Innovation Trends in Computer Science and Communication(IJRITCC), ISSN 2321-8169, pp 4078-4081 December, 2014.
[18]. Jianhao Tan and Jing Zhang “An Improved Clustering Algorithm Based on Density Distribution Function” Computer and Information Science Vol. 3, No. 3; August 2010.
[19]. He Zengyou, Xu Xiaofei , Deng Shengchun, Squeezer, “An efficient algorithm for clustering categorical data”, Journal of Computer Science and Technology, pp. 611-624, May 2002.
[20]. Shweta Verma, Vivek Badhe “Survey on Big Data and Mining Algorithm” International Journal of Scientific Research in Science, Engineering and Technology, Vol. 2 ,May 2016 Online ISSN : 2394-4099.

R. Prabahari, M. Ramalingam, "AN EMINENT WAY OF AN IMPROVING A DENCLUE ALGORITHM APPROACH FOR OUTLIER MINING IN LARGE DATABASE," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.536-540, 2018.

Switching the control of an electric motor with help of a Bluetooth device has been carried out in the present work. Electric motor works on electricity whose supply is controlled by a mechanical switch. In the present work switch is replaced by electronically operated transistor which is further controlled by a smart phone with help of a Bluetooth device. The wireless control action is achieved by assembling the prototype device which includes Bluetooth module, microcontroller, transistor and electric motor. A simple self explanatory software program is written for controlling the on/off action of motor. Present work is helpful for the beginners as well as first year student of computer science.

Bluetooth, Electric motor and microcontroller

[1] A. Saravanan, A. Subhashini, “ZIGBEE Controlled Industrial Robot for Controlling the Fire in a Sensitive Way”, International Journal of Computer Sciences and Engineering, Vol.6, Issue 7, pp.1082-1084, 2018
[2] Pravin Bhadane, Pooja Patil, Nisha Singh, Priya Mishra, “Wireless Control of Electric Motor”, Vol.6, Issue 3, pp.598-602, 2018
[3] K. Ravikumar, V. Manikandan, “Detection of Node Capture Attack in Wireless Sensor Networks”, Int. J. of Scientific Research in Computer Science and engineering (IJSRCSE), Vol.6, Issue.4, pp.56-61, 2018.
[4] K. Kaushik, B. Bhushan, R. Sharma, “Energy Constrained Routing Algorithm for Wireless Networks”, Int. J. of Scientific Research in Computer Science and Engineering (IJSRCSE), Vol. 5, Issue. 2, pp.33-35, 2017.
[5] Rajesh Verma, New Approach for Sampling Mobile Phone Accelerometer Sensor Data for Daily Mood Assessment, International Journal of Scientific Research in Network Security and Communication (IJSRNSC), Vol.1, Issue.3, pp.16-20, 2013
[6] V. Parashar, Article Use of ICT in Agriculture, International Journal of Scientific Research in Network Security and Communication (IJSRNSC) Vol.4, Issue.5, pp.8-11, 2016.
[7] Pratibha Singh, Dipesh Sharma, Sonu Agrawal, “A modern study of Bluetooth wireless technology”, Int. J. of Computer Science, Engineering and Information Technology, vol.1, Issue 3, p55-63, 2011.
[8] http://www.circuitstoday.com/story-and-history-of-development-of-arduino.

Pravin Bhadane, Pooja Patil , Nisha Singh, Priya Mishra, "CONTROL OF ELECTRIC MOTOR USING BLUETOOTH," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.541-544, 2018.