Volume-06 , Special Issue-11 , Dec - 2018 Go Back

Cloud Computing is an important field in today’s computer world. Data are entered the server through various algorithms and each algorithm has its own advantages and disadvantages. In this paper two algorithms are compared, and their energy efficiency based on their time taken is found and a graph is tabulated. The algorithms are Round Robin and Virtual Machine migration algorithms.

Cloud Computing, Machine Migration, Virtual data Round Robin

[1] A. Ali-Eldin, J. Tordsson, and E. Elmroth, “An adaptive hybrid elasticity controller for cloud infrastructures,” in Proc. of Network Operations and Management Symposium (NOMS), 2012, pp. 204–212.
[2] A. Sulistio, K. H. Kim, and R. Buyya, “Managing cancellations and no-shows of reservations with overbooking to increase resource revenue,” in Proc. of Intl. Symposium on Cluster Computing and the Grid (CCGrid), 2008, pp. 267–276.
[3] L. Tom´as and J. Tordsson, “Improving Cloud Infrastructure Utilization through Overbooking,” in Proc. of ACM Cloud and Autonomic Computing Conference (CAC), 2013.
[4] “Cloudy with a chance of load spikes: Admission control with fuzzy risk assessments,” in Proc. of 6th IEEE/ACM Intl. Conference on Utility and Cloud Computing, 2013, pp. 155–162.
[5] K. J. A° stro¨m and R. M. Murray, Feedback Systems: An Introduction for Scientists and Engineers. Princeton University Press, 2008.

P.S.S. Akilasri, K. Meenakshi, "Comparison between Round Robin and Virtual Migration Algorithm Based on their Energy Efficiency", International Journal of Computer Sciences and Engineering, Vol.06, Issue.11, pp.196-200, 2018.

In this paper presents the term what exactly means “Big data”. we first investigate how big data is, and what are the recent technologies developed for big data. Due to this, we identify the big data applications including enterprise management, Internet of Things, online social networks, media and entrainment and healthcare. The various Challenges faced in large data management include scalability, unstructured data, accessibility, real time analytics, fault tolerance and many more. This survey is concluded with problems to be identified and future directions.

big data, Internet of Things, Challenges

[1] D. P. Acharjya,andKauser Ahmed P “A Survey on Big Data Analytics: Challenges, Open Research Issues and Tools” (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 7, No. 2, 2016
[2] KuchipudiSravanthi,,and TatireddySubba Reddy,” Applications of Big data in Various Fields” (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 6 (5) , 2015, 4629-4632
[3] J.Archenaa and E.A.Mary Anita “A Survey Of Big Data Analytics in Healthcare and Government” Procedia Computer Science 50 ( 2015 ) 408 – 413
[4] Chun‑Wei Tsai1, Chin‑Feng Lai, Han‑Chieh Chao and Athanasios V. Vasilakos“Big data analytics: a survey” Tsai et al. Journal of Big Data (2015) 2:21
[5] Amir Gandomi, MurtazaHaider“Beyond the hype: Big data concepts, methods, and analytics” International Journal of Information Management 35 (2015) 137–144
[6] Min Chen • Shiwen Mao • Yunhao Liu“Big Data: A Survey” Mobile NetwAppl (2014) 19:171–209
[7] Nawsher Khan,1,2 Ibrar Yaqoob,1 Ibrahim AbakerTargio Hashem,1Zakira Inayat,1,3 Waleed KamaleldinMahmoud Ali,1 Muhammad Alam,4,5Muhammad Shiraz,1 and Abdullah Gani1 “Big Data: Survey, Technologies, Opportunities, and Challenges”, The Scientific World JournalVolume 2014, Article ID 712826, 18 pages
[8] CheikhKacfahEmani, Nadine Cullot, Christophe Nicolle “Understandable Big Data: A survey” Computer Science Review 17 (2015) 70-81
[9] Information Management 35 (2015) 137–144
[10] Ahmed Oussousa , Fatima-Zahra Benjelloun a , Ayoub Ait Lahcena,b, , Samir Belfkih a “Big Data technologies: A survey” Journal of King Saud University – Computer and Information Sciences xxx (2017) xxx–xxx
[11] ElisabettaRaguseo “Big data technologies: An empirical investigation on their adoption, benefits and risks for companies” International Journal of Information Management 38 (2018) 187–195
[12] Ibrahim AbakerTargio Hashem a,n , IbrarYaqoob a , Nor BadrulAnuar a , Salimah Mokhtar a , Abdullah Gani a , Samee Ullah Khan b”The rise of “big data” on cloud computing: Review and open research issues “Information Systems 47 (2015)98–115
[13] Jinchuan CHEN, Yueguo CHEN, Xiaoyong DU, Cuiping LI, JiahengLU,Suyun ZHAO, Xuan Zhou “Big data challenge: adata management perspective” front comput. Sci.,2013,7(2),:157-164
[14] Harshawardhan S. Bhosale1 ,Prof.Devendra P. Gadekar2 “A review paper on big data and Hadoop” international journal of scientific and research publications, volume 4, issue 10, october 2014 756 ISSN 2250-3153.
[15] S. Justin Samuel1 ,Koundinya RVP2 , Kotha Sashidhar3 and C.R. Bharathi4 “A survey on big data and its research challenges” ARPN Journal of Engineering and Applied Sciences Vol. 10, no. 8, may 2015 ISSN 1819-6608.
[16] Ms.VibhavariChavan, Prof.Rajesh.N.Phursule“Survey Paper On Big Data” International Journal of Computer Science and Information Technologies, Vol. 5 (6) , 2014, 7932-7939
[17] Manjit Kaur and Shilpa “BIG Data and Methodology-A review” © 2013, IJARCSSE , Volume 3, Issue 10, October 2013

M. Saraswathi, "A. Ali-Eldin, J. Tordsson, and E. Elmroth, “An adaptive hybrid elasticity controller for cloud infrastructures,” in Proc. of Network Operations and Management Symposium (NOMS), 2012, pp. 204–212. [2]", International Journal of Computer Sciences and Engineering, Vol.06, Issue.11, pp.201-205, 2018.

Consumption and demand for agricultural produce is always on high variable ends. Consumption of an agricultural produce increases the demand among consumers and also vice versa. The need for a system to predict on demand for commodity is always felt among farming commodity such that the demand for agricultural commodity can be predicted earlier and hence supported earlier. This research works on application of social computing models over understanding the trends of consumer for prediction of demand. This research works on base of online marketing along with commodity utilization demand is discussed. Subsequently, ways to improve online marketing using the concepts of social computing are proposed and implemented. Analysis predicts on application of multiple soft computational models employed over traditional online marketing methods to suggest of effective / accurate prediction.

Social Computing, Agricultural Commodity, Agricultural Demand / Supply

[1] Arunkumar Thangavelu, N Manoharan, Design and Analysis of an Effective Channel Distribution Approach for Agricultural Commodities using MongoDB, Indian Journal of Science and Technology, Volume 9, Issue 47, 2018
[2] Chandra, R., Iyer, R. S., & Raman, R. (2015). “Enabling organizations to implement smarter, customized social computing platforms by leveraging knowledge flow patterns” In Journal of Knowledge Management, 19(1), 95-107
[3] Evans, N.D. (2010), “Application modernization and outsourcing-the adoption of social computing in the enterprise” In UNISYS, Tech Republic
[4] Chandra, R., Iyer, R.S. and Raman, R. (2013), “Social computing platforms leveraging knowledge flow patterns” In ICKM conference proceedings, pp. 29-39.
[5] W. Mason, J. W. Vaughan, and H. Wallach. (2013). “Computational social science and social computing” In Journal of Machine Learning, pages 1–4.
[6] Fei-Yue Wang, Xiaochen Li, Wenji Mao, Tao Wang. (2012). “Social Computing: Methods and Applications” In Zhejiang University Press, Hangzhou
[7] X Wang, L Li, Y Yuan, P Ye, FY Wang. (2016). “ACP based social computing and parallel intelligence” In CAAI Transactions on Intelligence Technology pp 377-393
[8] Steven J. Jackson, Tarleton Gillespie, and Sandy Payette. (2014). “The policy knot: Re-integrating policy, practice and design in CSCW studies of social computing” In Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing (CSCW `14). ACM, New York, NY, USA, pp. 588–602.
[9] Gunasundari Anantharaj, Arunkumar Thangavelu, A Predictive analytical approach towards improving the crop growth yield using fuzzy cognitive maps – CROYAN, IIOAB, Vol. 6,No-4, pgs: 120–130, 2015
[10]Shilton K., Koepfler Jes A., Fleischmann Kenneth R. (2014). “How to see values in social computing: methods for studying values dimensions” In Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing, February 15-19 2014, Baltimore, Maryland, USA
[11] Koch M, Schwabe G, Briggs R O. (2015). “CSCW and Social Computing” In Business and Information Systems Engineering Vol 57 Issue 3 pp 149-153
[12] Jin R., Zhang H., Zhang Y. (2018). “The Uncertainty problem in Social Computing and its Solution Method” In 2018 International Conference on Robots and Intelligent Systems (ICRIS) pp 517-521
[13] Sakthikumar Subramanian, Arunkumar Thangavelu, FACT-An adaptive customer churn rate prediction method using fuzzy muti-criteria classification approach for decision making, Asian Journal of Science and Technology, Volume-4, Issue-11, pg: 227-233, 2011
[14] Tang M., Zhu H., Mao X. (2015). “A Lightweight social computing approach to emergency management policy selection” In IEEE Transactions on Systems, Man and Cybernetics 2016 Vol 46 Issue 8
[15] Yu R., Prakash S. (2017). “Optimized online marketing and scheduling systems and methods that are based on demand driven results” In US Patent US9741021B2
[16] Padmanabhan N. (2018). “Online Marketing- Present Scenario” In International Journal of Scientific Research Vol 7 No 4(2018)
[17] Susanne Schwarzl and Monika Grabowska. (2015), “Online Marketing Strategies: the future is here” In Journal of international studies, Volume :8, No.2, pp.187 – 196.
[18] Wu, J., N. Wen, W. Dou, and J. Chen. (2015). “Exploring the Effectiveness of Consumer Creativity in Online Marketing Communications” In European Journal of Marketing 49 (1/2): 262–276.
[19] G Roy, B Datta, R Basu. (2017). “Trends and future directions in online marketing research” In Journal of Internet Commerce Vol 16 Issue 1
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[21] Stoliartchouk A., Jordan F., Box B. (2017). “System and method for dynamic management of affiliate links for online marketing” US Patent US9779425B2
[22] Ting T., Davis J., Pettit F. (2014). “Online marketing research utilizing sentiment analysis and tunable demographics analysis” US Patent US8694357B2
[23] Mitran A., Negricea C., Edu T. (2014). “Modelling the Influence of Online Marketing Communication on Behavioural Intentions”, Network Intelligence Studies Volume II, 2 (4), 245-253.
[24] Gurau C. (2008). “Integrated online marketing communication: implementation and management” Journal of communication management Vol 12 Issue 2

Nithya Ganapathi Subramanian, P.S.S. Akhilashri, "An Analysis on Methods to Predict On Demand Based Online Agricultural Commodity of Selling Buying Using Social Computational Driven Models", International Journal of Computer Sciences and Engineering, Vol.06, Issue.11, pp.206-209, 2018.

Sentiment analysis is an application of natural language processing. It is also known as emotion extraction or opinion mining. Sentiment analysis or opinion mining is the computational study of opinions, sentiments and emotions. Opinions are usually particular expressions that designate people’s sentiments, judgments’ or approach toward entities, events and their properties. In general, opinions can be expressed on anything, e.g., a product, a service, an individual, an organization, an event, or a topic. In this paper, the SA classification levels, approaches are discussed. It also reports about various categories of tools used to process the sentimental analysis data. And various application and challenges in sentimental analysis are explained.

Senitmental Analysis, NLP, Opinion Mining

[1] Padmaja, S., & Fatima, S. S. (2013). Opinion Mining and Sentiment Analysis–An Assessment of Peoples’ Belief: A Survey. International Journal.
[2] AlessiaD`Andrea, Fernando Ferri, PatriziaGrifoni, TizianaGuzzo(2015) Approaches, Tools and Applications for Sentiment Analysis Implementation, International Journal of Computer Applications(09758887),vol.125)
[3] Medhat, W., Hassan, A., Korashy, H. 2014. “Sentiment analysis algorithms and applications: A survey”, Ain Shams Eng.
[4] Ashish Katrekar, AVP, Big Data Analytics “ an introduction to sentimental analysis”, GlobalLogic Inc, www.globallogic,com. – FIGURE 1.
[5] Maynard, D., & Funk, A. 2011. Automatic detection of political opinions in tweets. In: Proceedings of the 8th international conference on the semantic web, ESWC’11, p. 88-99.
[6] Pang, B., Lee, L., Vaithyanathan, S. 2002. Thumbs up? Sentiment Classification using Machine Learning Techniques. Proc. of 7th EMNLP, pp.79-86.
[7] Michael W. Berry, Soft Computing in Data Science, First International Conference, Scds 2015, Putrajaya, Malaysia, September 2-3, 2015, Proceedings (Communications in Computer and Information Science)
[8] Vishal vyas , Uma, “An Extensive study of Sentiment Analysis tools and Binary Classification of tweets using Rapid Miner”, 6th International Conference on Smart Computing and Communications, ICSCC 2017, 7-8 December 2017, Kurukshetra, India, Procedia Computer Science 125 (2018) 329–335.
[9] K. Ravi, V. Ravi , “A survey on opinion mining and sentiment analysis: tasks, approaches and applications” Knowledge-Based Systems (2015), doi: http://dx.doi.org/10.1016/j.knosys.2015.06.015
[10] Lucas Montesano’s; S. Juan Pablo Rodrguez ; Marcos Orchard ; Susana Eyheramendy,” Sentiment analysis and prediction of events in TWITTER”, CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON) , Page(s):903 - 910 , Oct 2015.
[11] Akshi Kumar, Prakhar Dogra and Vikrant Dabas,”Emotion Analysis of Twitter using Opinion Mining”,IEEE,978-1-4673-7948-9/15,2015
[12] B. Lue, Sentiment Analysis and Opinion Mining (Morgan & Claypool Publishers, 2012).
[13] Osamah A.M Ghaleb ,Anna Saro Vijendran, “THE CHALLENGES OF SENTIMENT ANALYSIS ON SOCIAL WEB COMMUNITIES” international journal on advanced researchin science and engineering vol 6, issue 12, dec 2017
[14] Harshali P. Patil1 and Mohammad Atique “Applications, Issues and Challenges in Sentiment analysis and Opinion Mining– A User’s perspective” international journal of control theory and aplications. International science press Vol 10. Number 19 2017.

S. Thulasi Bharathi, S. Charles, "Classification Levels, Approaches, Tools, Application and Challenges in Sentimental Analysis- A Survey", International Journal of Computer Sciences and Engineering, Vol.06, Issue.11, pp.210-215, 2018.

Classification is an important data mining technique based on machine learning with broad applications. It classifies various kinds of data and used in nearly every field of our life. Classification is used to classify every item in a set of data into one of predefined set of classes or groups. This paper describes the performance analysis of Naïve Bayes and J48 classification algorithm based on the correct and incorrect instances of data classification. Naive Bayes algorithm is based on probability and j48 algorithm is based on decision tree. In this paper we compare and perform evaluation of classifiers NAIVE BAYES and J48 in the context of mushroom dataset in UCI repository to maximize true positive rate and minimize false positive rate of defaulters rather than achieving only higher classification accuracy using WEKA. The experiments results shown in this paper are about true positive rate, false positive rate, classification accuracy and cost analysis. The results in the paper on mushroom dataset in UCI repository performance best in WEKA tools also show that the efficiency and accuracy of J48 than Naive Bayes is good.

Data mining, Weka Tool, J48,Navie Bayes

[1] Huaifeng Zhang, Yanchang Zhao, Longbing Cao and Chengqi Zhang, “Combined Association Rule Mining”, PAKDD 2008, LNAI 5012, pp. 1069-1074, 2008 © Springer- Verlag Berlin Heidelberg 2008
[2] Pratima Gautam and K.R. Pardasani, “Algorithm for Efficient Multilevel Association Rule Mining” In (IJCSE) International Journal on Computer Science and Engineering, Volume 02, No. 05, 1700-1704, 2010.
[3] Xunwei Zhou and Hong Bao ,” Mning Double-Connective Association Rules from Multiple Tables of Relational Databases “ In IEEE,2008
[4] Raja Tlili and Yahya Slimani, “Executing Association Rule Mining Algorithm under a Gird Computing Environment” In PADTAD,July 2011.
[5] Anis Suhailis Abdul Kadir, Azuraliza Abu Bakar and Abdul Razak Hamdan, “Frequent Absence and Presence Itemset for Negative Association Rule Mining “, IEEE,2011.
[6] Guimei Liu, Haojun Zhang and Limsoon Wong, “Controlling False Positives Iin Association Rule Mining” In Proceedings of the VLDB Endowment ACM,2011.
[7] Somboon Anekritmongkol and M. L. Kulthon Kasamsan , “ The Comparative of Boolean Algebra Compress and Apriori Rule Techniques for New Theoretic Association Rule Mining Model” In IEEE,2009
[8] Salama, G.I.; Abdelhalim, M.B.; Zeid, M.A., "Experimental comparison of classifiers for breast cancer diagnosis," Computer Engineering & Systems (ICCES), 2012 Seventh International Conference on , vol., no., pp.180,185, 27-29 Nov.,2012.
[9] S. Moertini Veronica ,”Towards The Use Of C4.5 Algorithm For Classifying Banking Dataset”,Integeral Vol 8 No 2,October 2013.
[10] UM, Ashwinkumar, and Anandakumar KR. "Predicting Early Detection of Cardiac and Diabetes Symptoms using Data Mining Techniques.",IEEE,pp:161-165,2011
[11] Geetha Ramani R, Lakshmi Balasubramanian, and Shomona Gracia Jacob. "Automatic prediction of Diabetic Retinopathy and Glaucoma through retinal image analysis and data mining techniques." In Machine Vision and Image Processing (MVIP), 2012 International Conference on, pp. 149-152. IEEE, 2012
[12] Sugimoto, Masahiro, Masahiro Takada and Masakazu Toi. "Comparison of robustness against missing values of alternative decision tree and multiple logistic regression for predicting clinical data in primary breast cancer." In Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE, pp. 3054-3057. IEEE, 2013.
[13] Hussein Asmaa S,Wail M. Omar, Xue Li, and Modafar Ati. "Efficient Chronic Disease Diagnosis prediction and recommendation system." In Biomedical Engineering and Sciences (IECBES), 2012 IEEE EMBS Conference on, pp. 209-214. IEEE, 2012.
[14] Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.
[15] Korting, Thales Sehn. "C4. 5 algorithm and Multivariate Decision Trees." Image Processing Division, National Institute for Space Research--INPE.
[16] Guo, Yang, Guohua Bai, and Yan Hu. "Using Bayes Network for Prediction of Type-2 Diabetes." In Internet Technology And Secured Transactions, 2012 International Conferece For, pp. 471-472. IEEE, 2012.

M. Senthamilselvi, P.S.S. Akilashri, "Association Rule Mining Classification using J48 & Navie Bayes", International Journal of Computer Sciences and Engineering, Vol.06, Issue.11, pp.216-220, 2018.

Five totally different threshold segmentation primarily based approaches are reviewed and compared up here to extract the growth from set of brain pictures. This analysis focuses on the analysis of image segmentation ways, a comparison of 5 semi-automated ways are undertaken for evaluating their relative performance within the segmentation of growth. Consequently, results square measure compared on the idea of quantitative and analysis of individual ways. The aim of this study was to analytically determine the ways, most fitted for application for a selected genre of issues. The results show that of the region growing segmentation performed on top of rest in most cases.

Tumor, MRI, Region Growing, Segmentation, Watershed, FCM

[1] R.A. Gonzalez, R.E. Woods, “Digital Image Processing”. Second Edition. Prentice Hall 2002 [2] R. N. Strickland, “Image-Processing Techniques for Tumor Detection”, University of Arizona Tucson, Arizona.2002
[3] Natarajan P, Krishnan.N, Natasha S, Shraiya N, Bhuvanesh P, “Tumor Detection using threshold operation in MRI Brain Images”, International Conference on Computational Intelligence and Computing Research,IEEE, 2012,
[4] Manoj K and Sourabh Y, “Brain Tumor Detection and Segmentation Using Histogram Thresholding”, International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-1, Issue-4, April 2012
[5] M. Usman A, Anam. U. “Computer Aided system for Brain Tumor Detection and Segmentation” Computer Networks and information Technology (ICCNIT), IEEE, 2011.
[6] G. M. N. R. Gajanayake, R. D. Yapa1 and B. Hewawithana, “Comparison of Standard Image Segmentation Methods for Segmentation of Brain Tumors from 2D MR Images”, 4th International Conference on Industrial and Information Systems, ICIIS, University of Peradeniya, Sri Lanka, pp. 301- 305., IEEE, 2009.
[7] B. N. Saha, N. Ray, R. Greiner, A. Murtha, H. Zhang, “Quick detection of brain tumors and edemas: A bounding box method using symmetry”, Computerized Medical Imaging and Graphics 36 (2012) 95– 107, Elsevier, 2011
[8] M. C. Clark, L. O. Hall, D. B. Goldgof, R. Velthuizen, F. Reed Murtagh, and M. S. Silbiger, “Automatic Tumor Segmentation Using Knowledge-Based Techniques”, IEEE transactions on medical imaging, vol. 17, no. 2, april 1998
[9] C.L. Biji, D. Selvathi, and A. Panicker, “Tumor Detection in Brain Magnetic Resonance Images Using Modified Thresholding Techniques”, A. Abraham et al. (Eds.): ACC 2011, Part IV, CCIS 193, pp. 300–308, 2011, Springer-Verlag Berlin Heidelberg 2011
[10] J. Fan, G. Zeng, M. Body, M. Hacid, “Seeded region growing: an extensive and comparative study”, Pattern Recognition Letters 26 1139–1156, Elsevier, 2005.
[11] Yu-len huang and Dar-ren chen,” Watershed segmentation for breast tumor in 2-d sonography”, Ultrasound in Med. & Biol., Vol. 30, No. 5, pp. 625– 632, Elsevier, 2004.
[12] N. Gordillo, E. Montseny, P. Sobrevilla, “State of the art survey on MRI brain tumor segmentation”, journal of magnetic resonance imaging, Elsevier, 2013.
[13] R. Adam and L. Bischof, “Seeded region growing”, IEEE transaction on PAMI, Vol.16, 6.June.1994.
[14] M. M. Synthuja, J. Preetha, Dr. L.Padma Suresh, M. J. Bosco, “Image Segmentation Using Seeded Region Growing”, 2012 International Conference on Computing, Electronics and Electrical Technologies [ICCEET], IEEE 2012.
[15] Mustaqeem, A. Javed, T. Fatima, “An Efficient Brain Tumor Detection Algorithm Using Watershed & Thresholding Based Segmentation”, I.J. Image, Graphics and Signal Processing, 2012, 10, 34-39, MECS, 2012
[16] M. A. Balafar, A. R. Ramli, M. I. Saripan, S. Mashohor, “Review of brain MRI image segmentation methods”, Springer Science and Business Media B.V. 2010, Artif Intell Rev, 33:261–274, 2010
[17] Meyer F. and Beucher S., “Morphological Segmentation”, journal of visual communication and image representation, Vol. 1, pp. 21-46, Sept. 1990 academic press.

S. Josephine, S. Murugan, "Comparative Analysis Tumor Detection and Segmentation Exploitation Watershed Technique", International Journal of Computer Sciences and Engineering, Vol.06, Issue.11, pp.221-227, 2018.

Nowadays garbage collection is a major problem in all cities. Even though there are big garbage bin in each street, it overflows and bad odour makes that area very uncomfortable. The concept of IOT is used in various applications. Here this paper uses IOT for the separation of waste by keeping different types of smart bins for plastics, glass or iron and organic wastes in each street. So that at the time of disposing the waste the bin senses the waste material through the sensor. There is a motor and sensors on the top of it that identify the type of waste. Here one moisturizer sensor is used for segregating the dry and wet waste. Once selection is decided the robotic arms push the wastes to the respective box in various methods. Through this the disposal of garbage can be made efficiently according to the various types of waste materials.

Smart Bin, Sensors, Segregation, IOT (Internet of Things)

[1] S.S. Navghane, M.S. Killedar, Dr.V.M. Rohokale, “IoT Based Garbage and Waste Collection Bin”,   International Journal of Advance Research, Ideas and Innovations in Technology ISSN: 2454-132X (Volume 4, Issue 2) May 2016.
[2] Vikrant Bhor, Pankaj Morajkar, Maheshwar Gurav, Dishant Pandya, ―”Smart Garbage Management System”, International Journal of Engineering Research and Technology(IJERT) ISSN: 2278-0181Vol.4 Issue 03, March 2015.
[3] Alexey Medvedev, Petr Fedchenkov, ArkadyZaslavsky, Theodoros, Anagnostopoulos Sergey Khoruzhnikov, ”Waste Management as an IOT Enabled Service in Smart Cities”.
[4] Microtronics Technologies, ―GSM based garbage and waste collection bins overflow indicator‖, /International Journal of Pharmacy & Technology, Vol. 8, Issue No.4, Dec-2016.
[5] Prof. Ramshaw, AkshayGoads, PremedShined, ResumeShined, "Garbage and Street Light Monitoring System Using Internet of Things" International Journal of Innovative Research In Electrical, Electronics, Instrumentation And Control Engineering, Vol. 4, Issue 4, April 2016.
[6] “A Review on Solid Waste Management using SmartBin” International Journal of Innovative Research in Computer and Communication Engineering Vol. 4, Issue 11, November 2016.
[7] Md. Shafiqul Islam, M. A. Hannan, Maher Arebey, HasanBasri, " Smart Garbage Monitoring System using Internet of Things (IOT)", International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering Vol. 5, Issue 1, January 2017.
[8] Twinkle Sinha, K.Mugesh Kumar, P.Saisharan, "Smart Dustbin", International Journal of Industrial Electronics and Electrical Engineering, Volume – 1, Issue – 6, Sep - Oct 2017.
[9] Q. F. Huang, Q. Wang, L. Dong, B. D. Xi, and B. Y. Zhou, “The current situation of solid waste management in China”, Journal of Material Cycles and Waste Management - J MATER CYCLES WASTE MANAG, vol. 8, no. 1, pp. 63-69, 2006.
[10] “IOT Based Garbage Detecting/Monitoring and Segregating System” - International Journal of Science Technology & Engineering, Volume 4, Issue 7, January 2018

M. Radhika, "Smart Bin for the Separation of Waste using IOT", International Journal of Computer Sciences and Engineering, Vol.06, Issue.11, pp.228-230, 2018.

Association rule mining (ARM) is used to advance decisions making in the application. ARM became important in an information and decision-overloaded world. They changed the way user make decisions, and helped their creators to increase revenue at the same time. Bringing ARM is essential in order to popularize them beyond the limits of scientific research and high technology entrepreneurship. This paper extracts attractive correlations frequent patterns and association among set of items in the transaction database. This paper describes the show analysis of Naïve Bayes and J48 classification algorithm based on the correct and incorrect instances of data classification. Naive Bayes is probability based and j48 algorithm is decision tree based. In this paper Comparison weka and orange by using tool to perform evaluation of classifiers NAIVE BAYES and J48 in the context of mushroom dataset in UCI repository to maximize true positive rate and minimize false positive rate of defaulters rather than achieving only higher classification accuracy using WEKA and orange tool. The experiments results reveals the true positive rate, false positive rate, classification accuracy and cost analysis. The results in the paper on mushroom dataset in UCI repository performance best in Weka than Orange tools. The efficiency and accuracy of J48 -than Naive Bayes is good

Data mining, Weka Tool, Orange tool J48,Navie Bayes

[1] Huaifeng Zhang, Yanchang Zhao, Longbing Cao and Chengqi Zhang, “Combined Association Rule Mining”, PAKDD 2008, LNAI 5012, pp. 1069-1074, 2008 © Springer- Verlag Berlin Heidelberg 2008
[2] Pratima Gautam and K.R. Pardasani, “Algorithm for Efficient Multilevel Association Rule Mining” In (IJCSE) International Journal on Computer Science and Engineering, Volume 02, No. 05, 1700-1704, 2010.
[3] Xunwei Zhou and Hong Bao ,” Mning Double-Connective Association Rules from Multiple Tables of Relational Databases “ In IEEE,2008
[4] Raja Tlili and Yahya Slimani, “Executing Association Rule Mining Algorithm under a Gird Computing Environment” In PADTAD,July 2011.
[5] Anis Suhailis Abdul Kadir, Azuraliza Abu Bakar and Abdul Razak Hamdan, “Frequent Absence and Presence Itemset for Negative Association Rule Mining “, IEEE,2011.
[6] Guimei Liu, Haojun Zhang and Limsoon Wong, “Controlling False Positives Iin Association Rule Mining” In Proceedings of the VLDB Endowment ACM,2011.
[7] Somboon Anekritmongkol and M. L. Kulthon Kasamsan , “ The Comparative of Boolean Algebra Compress and Apriori Rule Techniques for New Theoretic Association Rule Mining Model” In IEEE,2009
[8] Salama, G.I.; Abdelhalim, M.B.; Zeid, M.A., "Experimental comparison of classifiers for breast cancer diagnosis," Computer Engineering & Systems (ICCES), 2012 Seventh International Conference on , vol., no., pp.180,185, 27-29 Nov.,2012.
[9] S. Moertini Veronica ,”Towards The Use Of C4.5 Algorithm For Classifying Banking Dataset”,Integeral Vol 8 No 2,October 2013.
[10] UM, Ashwinkumar, and Anandakumar KR. "Predicting Early Detection of Cardiac and Diabetes Symptoms using Data Mining Techniques.",IEEE,pp:161-165,2011
[11] Geetha Ramani R, Lakshmi Balasubramanian, and Shomona Gracia Jacob. "Automatic prediction of Diabetic Retinopathy and Glaucoma through retinal image analysis and data mining techniques." In Machine Vision and Image Processing (MVIP), 2012 International Conference on, pp. 149-152. IEEE, 2012
[12] Sugimoto, Masahiro, Masahiro Takada and Masakazu Toi. "Comparison of robustness against missing values of alternative decision tree and multiple logistic regression for predicting clinical data in primary breast cancer." In Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE, pp. 3054-3057. IEEE, 2013.
[13] Hussein Asmaa S,Wail M. Omar, Xue Li, and Modafar Ati. "Efficient Chronic Disease Diagnosis prediction and recommendation system." In Biomedical Engineering and Sciences (IECBES), 2012 IEEE EMBS Conference on, pp. 209-214. IEEE, 2012.
[14] Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.
[15] Korting, Thales Sehn. "C4. 5 algorithm and Multivariate Decision Trees." Image Processing Division, National Institute for Space Research--INPE.
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M. Senthamilselvi, P.S.S. Akilashri, "A Comparative Study on Weka, Orange Tool for Mushroom Data Set", International Journal of Computer Sciences and Engineering, Vol.06, Issue.11, pp.231-236, 2018.

Because of the huge information in biomedical and healthcare communities, correct study of medical data benefits early disease detection, community services and patient care. The exactness of study is reduced when the value of medical data is incomplete. Moreover, various regions exhibit unique appearances of particular regional diseases, those results in weakening the prediction of disease outbreaks. In the proposed system, it provides machine learning algorithms for effective prediction of thyroid disease occurrences in disease-frequent societies. It experiment the changed models over real-life hospital data collected. To overcome the difficulty of incomplete data, it uses a latent factor model to rebuild the missing data. It experiment on a thyroid diseases using structured and unstructured data from hospital it use FR-Growth and Decision Tree algorithm. Compared to several typical estimate algorithms, the calculation exactness of our proposed FP Growth algorithm reaches 98.8% with a convergence speed which is faster than that of the decision tree algorithm on disease risk prediction on thyroid using Weka tool.

Data mining, Machine Learning, Decision Tree

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M.Shyamala, P.S.S. Akilashri, "Thyroid Disease Prediction by Machine Learning Technique From Healthcare Communities", International Journal of Computer Sciences and Engineering, Vol.06, Issue.11, pp.237-242, 2018.

Digital images and videos are converted into the compressed forms for transmission. The compression and hiding the secret data in images is done because of a wide range of hackers. In order to overcome this, the messages are sent with the help of images. Many data hiding schemes have been found in the recent times with various compression techniques in digital images. Vector Quantization method help in error distortion and error diffusion which are a cause of progressive diffusion. The new idea is that one can side match vector quantization (SMVQ) and image inpainting. Data hiding and image compression can be integrated into a single module to sent the message. The receiver then finds the secret message using the image decompression technique through the index values in the segmented sections.

Image Compression, Side Match Vector Quantization (SMVQ), Data Hiding, Image In Painting

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P.S.S. Akilashri, B. M .Kannan, "Data-Hiding and Compression Schema Based on Image Inpainting", International Journal of Computer Sciences and Engineering, Vol.06, Issue.11, pp.243-245, 2018.