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The proposed work presents an outline and execution of online fuzzy expert system for diabetes diagnosis (Web-FESDD). This work proposes a rule-based expert system where fuzzy logic was used. It was actualized online for the determination of diabetes disease using open source development environment. Doctors, diabetes experts and patients can utilize Web- FESDD for diabetes diagnosis as an intelligent diagnostic system. Fuzzy expert systems are able to handle imprecise data which occurs in process of disease diagnosis and treatment. Fuzzy Logic is highly suitable and applicable in designing expert systems in medicine context; especially in disease diagnosis procedure and in treatment plan. Open source programming advancement features and conditions were utilized to create and complete the proposed work.

Dibetes Mellitus, Expert System, Fuzzy Logic, Fuzzy Expert System

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[23] I K Mujawar, B T Jadhav and Kapil Patil.Web-based Fuzzy Expert System for Symptomatic Risk Assessment of Diabetes Mellitus. International Journal of Computer Applications 182(3):5-12, July 2018.

I.K. Mujawar, B.T. Jadhav, "Web-based Fuzzy Expert System for Diabetes Diagnosis," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.995-1000, 2019.

The optical phase characteristics of fiber Bragg grating is studied under the influence of the Kerr nonlinearity. The expression of optical phase has been obtained analytically under nonlinear regime using coupled mode theory. The optical phase is studied by plotting the phase factor as a function of wavelength at various input intensities. The results show that the phase of the propagating beam is altered after specific excitation intensity. Such variation in the optical phase of beam can be utilizing the grating as a nonlinear device for optical phase modulator in all optical signal processing.

Optical Phase, Transmittivity, Kerr Effect, Modulational Instabilities, Fiber Bragg Grating, Nonlinear Coupled Mode Equations

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Poornima Rawat, Santosh Pawar and Tryambak Hiwarkar, "Optical Phase Alteration in Nonlinear Fiber Bragg Grating," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.1001-1004, 2019.

It has been a real challenge for computers with low computing power and memory to detect objects in real time. After the invention of Convolution Neural Networks (CNN) it is easy for computers to detect images and recognize them. There are several technologies and models which can detect objects in real time, but most of them require high end technologies in terms of GPUs and TPUs. Though, recently many new algorithms and models have been proposed, which runs on low resources. In this paper we studied MobileNets to detect objects using webcam to successfully build a real time object detection system. We observed the pre trained model of the famous MS COCO dataset to achieve our purpose. Moreover, we applied Google’s open source TensorFlow as our back end. This real time object detection system may help in future to solve various complex vision problems

TensorFlow, MobileNet, MS COCO, Real-time, and Object detection

[1] S. Tripathi, G. Dane, B. Kang, V. Bhaskaran, and T. Nguyen, “LCDet: Low-Complexity Fully-Convolutional Neural Networks for Object Detection in Embedded Systems”, work done in part during an internship at Qualcomm, arXiv:1705.05922 [cs.CV], https://arxiv.org/abs/1705.05922 accessed on 27.09.2018, May 2017.
[2] Y. Li, J. Li, W. Lin, and J. Li, “Tiny-DSOD: Lightweight Object Detection for Resource-Restricted Usages”, Shanghai Jiao Tong University and Intel Labs, arXiv: 1807.11013 [cs.CV], https://arxiv.org/abs/1807.11013 accessed on 27.09.2018, July 2018.
[3] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You Only Look Once: Unified, Real-Time Object Detection”, arXiv: 1506.02640 [cs.CV], https://arxiv.org/abs/1506.02640 accessed on 27.09.2018, June 2015.
[4] X. Zhang, X. Zhou, M. Lin, and J. Sun, “ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices”, MegviiInc (Face++), arXiv:1707.01083 [cs.CV], https://arxiv.org/abs/1707.01083 accessed on 27.09.2018, Dec 2017.
[5] R. J. Wang, X. Li, S. Ao, and C. X. Ling, “Pelee: A Real-Time Object Detection System on Mobile Devices”, University of Western Ontario, arXiv: 1804.06882v1 [cs.CV], https://arxiv.org/abs/1804.06882 accessed on 27.09.2018, April 2018.
[6] S. Y. Nikouei, Y. Chen, S. Song, and T. R. Faughnan, “Kerman: A Hybrid Lightweight Tracking Algorithm to Enable Smart Surveillance as an Edge Service”, Binghamton University, arXiv: 1808.02134 [cs.DC], https://arxiv.org/abs/1808.02134 accessed on 27.09.2018, August 2018.
[7] T. Liu, M. Elmikaty, and T. Stathaki, “SAM-RCNN: Scale-Aware Multi-Resolution Multi-Channel Pedestrian Detection”, Electrical and Electronic Engineering Imperial College London and Jaguar Land Rover Research Coventy, arXiv: 1808.02246 [cs.CV], https://arxiv.org/abs/1808.02246 accessed on 27.09.2018, August 2018.
[8] T. Y. Lin, M. Maire, S. Belongie, L. Bourdev, R. Girshick, J. Hays, P. Perona, D. Ramanan, C. L. Zitnick, and P. Dollar, “Microsoft COCO: Common Objects in Context”, arXiv: 1405.0312 [cs.CV], https://arxiv.org/abs/1405.0312 accessed on 27.09.2018, May 2014.
[9] A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications”, arXiv: 1704.04861 [cs.CV], https://arxiv.org/abs/ 1704.04861 accessed on 27.09.2018, April 2017.
[10] M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Yuan Yu, X. Zheng ,“TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems”, Google Research, available on http://download.tensorflow.org/paper/whitepaper2015.pdf accessed on 27.09.2018, November 2015.
[11] J. E. Espinosa, S. A. Velastin, and J. W. Branch, “Motorcycle detection and classification in urban Scenarios using a model based on Faster R-CNN”, University Carlos 3 – Madrid Spain, arXiv: 1808.02299 [cs.CV], https://arxiv.org/abs/1808.02299 accessed on 27.09.2018, August 2018.
[12] M. Rahman, M. Islam, J. Calhoun, and M. Chowdhury,“Real-time Pedestrian Detection Approach with an Efficient Data Communication Bandwidth Strategy”, Clemson University, arXiv: 1808.09023 [cs.CV], https://arxiv.org/abs/1808.09023 accessed on 27.09.2018, August 2018.

Jimut Bahan Pal, Shalabh Agarwal, "Real Time Object Detection Can be Embedded on Low Powered Devices," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.1005-1009, 2019.

In the contemporary age of digitization, majority of the users are constantly moving on the prevalent computing in the area of telecommunication and social networking. The data may be produced from several resources from an individual to organization level. The existing data mining techniques are not suitable, due to the features of non structured and semi-structuredness in data which leads to dimensionality problems. To overcome these problems, an Efficient Document Subspace Clustering in High Dimensional Data using Fast Clustering Algorithm (EDSFCA) is proposed. This method performs Datamining techniques like preprocessing and removing of corrupted and repetative data from the subspace clusters. The twitter data is taken as an input and is divided into clusters in order to provide a characteristic of high-dimensional data. This information is organized arbitrarily in subspace clusters and then segmentation is done on data points. The EDSFCA approach does the cluster analysis of datasets in smallest period of time.

Data Mining, Fast Clustering Algorithm, High Dimensional Data, Subspace Clustering

[1] P. Buhlmann, S. van de Geer, “Statistics for High-Dimensional Data: Methods, Theory and Applications”, Springer Science & Business Media, 2011
[2] V. B. Canedo, N. S. Marono, A. A. Betanzos, “Feature Selection for High-Dimensional Data”, Springer-Computer, 2015
[3] Radhika K R, Pushpa C N, Thriveni J and Venugopal K R, “EDSC: Efficient Document Subspace Clustering Technique for High-Dimensional Data”, In proceedings of International Conference on Computational Techniques in Information and Communication Technologies, Delhi , PP. 11-13, March 2016.
[4] M Verleysen, “Learning High-Dimensional Data”, University atholique Louvain, Microelectronics laboratory, pp. 141-162, 2003.
[5] A Petukhov and I Kozlov, "Greedy Algorithm for Subspace Clustering from Corrupted and Incomplete Data", IEEE Transaction on Information Security, 2015.
[6] Amardeep Kaur and Amitava Datta. “A Novel Algorithm for Fast and Scalable Subspace Clustring in High Dimensional Data”, Journal of BigData, 2015.
[7] C Yang, D Robinson and R Vidal, "Sparse Subspace Clustering with Missing Entries", In Proceedings of the 32nd International Conference on Machine Learning, pp. 2463-2472, 2015.
[8] Singh Vijendra, “Efficient Clustering for High Dimensional Data:Subspace Based Clustering and Density Based Clustering”, Information Technology vol. 10, pp. 1092-1105, 2011.
[9] Lance Parson, Ehtesham Haque and Huan Liu, “Subspace Clustering for High Dimensional Data: A Review”, sigkdd Explorations, vol. 6, pp. 90-105, 2004.
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[12] Singh Vijendra and Sahoo Laxman, “Subspace clustering of High Dimensional Data: An Evolutionary Approach”, Applied Computational Intelligence and Soft Computing”, vol. 2013, Article ID 863146, pp. 12.
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[14] Y Wang, Y-X Wang, and A Singh, "Clustering Consistent Sparse Subspace Clustering", Carnegie Mellon University, USA, arXiv preprint arXiv: 1504.01046, 2015.
[15] J Wei, M Wang and Q Wu, “Study on Different Representation Methods for Subspace Segmentation”, International Journal of Grid Distribution Computing, Vol. 8, no.1, pp.259-268, 2015.
[16] C Giraud Taylor and Francis group, “Introduction to High-Dimensional Statistics”, xv+252 pp. ISBN: 978-1-482-23794-8 2014.
[17] R Agrawal, J Gehrke, D Gunopulos, and P Raghavan ,“Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications”. In Transaction of Data Mining and Knowledge Discovery, vol. 11, Issue. 1, pp. 5-33, 2005.
[18] N Tomašev, M Radovanović, D Mladenić and M Ivanović, "Hubness- based Clustering of High-dimensional Data", In Partitional Clustering Algorithms, Springer International Publishing, pp. 353-386, 2015.
[19] Shuyun Wang, Yingjie Fan, Chenghong Zhang, HeXiang Xu, Xiulan Hao and Yunfa Hu, “ Subspace Clustering of High Dimensional Data Streams”, In Proceedings of 7th IEEE/ACIS International Conference on Computer and Information Science (ICIS 2008), pp. 14-16, Portland, USA.
[20] Manolis C. Tsakiris and René Vida, “Abstract algebraic-geometric subspace clustering”, In 48th Asilomar Conference on Signals, Systems and Computers , EISSN: 1058-6393, 2-5 Nov. 2014, CA, USA.
[21] Ezgi Can Ozan and Serkan Kiranyaz, “K-Subspaces Quantization for Approximate Nearest Neighbor Search”, In IEEE Transactions on Knowledge and Data engineering, Vol. 28, No. 7, pp. 1722-1733, 2016.
[22] Han Zhai, Hongyan Zhang, Liangpei Zhang, Pingxiang Li and Antonio Plaza, “A New Sparse Subspace Clustering Algorithm for Hyperspectral Remote Sensing Imagery”, In proceedings of IEEE Geoscience and Remote Sensing Letters, vol. 14, Issue. 1, pp. 43 – 47, 2017.
[23] Shulin Wang, Fang Chen and Jianwen Fang, “Spectral clustering of high-dimensional data via Nonnegative Matrix Factorizationion”, In proceedings of International Joint Conference on Neural Network (IJCNN), pp. 12-17, 2015, Ireland.
[24] Junjian Zhang, Chun-Guang Li, Honggang Zhang and Jun Guo, “Low-rank and structured sparse subspace clustering”, n proceedings of Visual Communication and Image Processing (VCIP), pp. 27-30, 2016, China.
[25] Alexander Petukhov and Inna Kozlov, “Greedy algorithm for subspace clustering from corrupted and incomplete data”, In proceedings of International Conference on Sampling Theory and Applications (SampTA), pp. 25-29, 2015, USA.
[26] Ran He, Liang Wang, Zhenan Sun, Yingya Zhang and Bo Li, “Information Theoretic Subspace Clustering”, IEEE Transactions on Neural Networks and Learning Systems, vol. 27, Issue. 12, pp. 2643-2655, 2016.
[27] Yifan Fu, Junbin Gao, David Tien, Zhouchen Lin and Xia Hong, “ Tensor LRR and sparse coding-based subspace clustering”, In IEEETransactions on Neural Networks and Learning Systems, vol. 27, Issue. 10, pp. 2120-2133, 2016.

Radhika K R, Pushpa C N, Thriveni J, Venugopal K R, "(EDSFCA): Efficient Document Subspace Clustering in High-Dimensional Data using Fast Clustering Algorithm," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.1010-1015, 2019.

Software testing is a major component of software development lifecycle and it’s time-consuming. The testing used the particular time in testing is generally disturbed with generating the test cases and correctly testing them. Although some people apply k-means clustering algorithm to the test suite reduction, the algorithm is unstable and seldom considers the coverage rate of such test cases; as a result, it will waste various unnecessary testing times in redundant cases and always result in high cost. Model-based testing method is one of the testing categories in which the test cases derived from that the system describes efficient aspects of the system under test. When the model of the system is described explicitly, reversing system performed correctly, it can be used for the renewable artifact. For instance, the system can be used to generate an appropriate test set for the SUT. Such technique is called model-based testing (MBT).The different approaches are implemented and evaluated in order to determine its effectiveness in reducing the redundancy of test case generation. The purpose of this project is to generate the test cases, prioritize them.

Software testing, test suite reduction, code coverage

[1] FENG LIU, JUN ZHANG, ER-ZHOU ZHU, ”TEST-SUITE REDUCTION BASED ON K-MEDOIDS CLUSTERING ALGORITHM”, CONFERENCE ON CYBER-ENABLED DISTRIBUTED COMPUTING AND KNOWLEDGE DISCOVERY.
[2] KARTHEEK MUTHYALA,” A NOVEL APPROACH TO TEST SUITE REDUCTION USING DATA”, Indian Journal of Computer Science and Engineering (IJCSE), Computer Science and Information Systems, Birla Institute of Technology and Science, 2011.
[3] MOHAMMED AKOUR, IMAN AL JARRAH, AHMAD A. SAIFAN,”AN EFFICIENT APPROACH FOR TEST SUITE REDUCTIONUSING K-MEANS CLUSTERING”, Journal of Theoretical and Applied Information Technology,15th September 2018,Vol.96. No 17, 2018.
[4] Marwah Alian , Dima Suleiman , Adnan Shaout ,”Test Case Reduction Techniques – Survey”, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 7, No. 5, 2016.
[5] Yulei Pang , Xiaozhen Xue , Akbar SiamiNamin ,”Identifying Effective Test Cases Through K-means Clustering for Enhancing Regression Testing”,2013 12th International Conference on Machine Learning and Applications.
[6] Yogesh Singh, Arvinder Kaur, Bharti Suri,” Test Case Prioritization using Ant Colony Optimization”, DOI: 10.1145/1811226.1811238, July 2010 Volume 35 Number 4 DOI: 10.1145.2010.
[7] M. LAKSHMI PRASAD1, M. KEERTHI2, K. SAI SRIKAR3, V. DIVYA4,”Generating Optimized Pair wise Test Cases by using K-Means Algorithm,”ISSN 2348–2370 Vol.09, Issue.05, April-2017.
[8]J.J. Gutiérrez, M.J. Escalona, M. Mejías,”A Model Driven Approach for Functional Test Cases”, Elsevier 12 August 2015.
[9] Mohamed El-Attar, Hamza Luqman, Peter Karpati, Guttorm Sindre,” Extending the UML State charts Notation to Model Security Aspects”, Member, IEEE, and Andreas L. Opdahl, Member, IEEE, Springer July 2015.
[10] Anjali Sharma and Maninder Singh, “Generation of Automated Test Cases Using UML Modeling”, International Journal of Engineering Research & Technology (IJERT) Vol. 2 Issue 4, April - 2013 ISSN: 2278-0181.

Aadil Farooq, Pramod Jadhav, S.D.Joshi, "Model Driven Testing based on Functional Test Case Generation with Redundancy Check," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.1016-1019, 2019.

The changes in weather and climate conditions have always affected crop cultivation, farming and animal breeding. Measures put in place sometimes fail. Information and cognitive technologies are innovative techniques that can be leveraged to combat these changes by applying precision agriculture. In this paper discussion is on future of precision agriculture which has been proven to work in other countries using machine learning & artificial intelligence. The scope of utilization is focused on medium and large scale farmers with an aim to point out the advantages and disadvantages of the techniques. Previously there has been a slow growth in this sector but from the year 2016 onwards many start ups have been emerging which are yielding high investments. These cognitive technologies have been applied in advanced countries and have resulted in increased yield, growth in GDP, low mortality rates and improved living standards. The same can be applied locally to boost production in the agricultural sector.

precision agriculture, Artificial intelligence, Machine learning, promising solutions

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Victor Mokaya, "Future of Precision Agriculture in India using Machine learning and Artificial Intelligence," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.1020-1023, 2019.

The smartphone industry is the fastest-growing sector in India. The smartphone industry has taken a long journey of 25 years from 1992 to 2018. During this 25 years of journey, mobile phones were transformed into personal computers. . It is growing at a very rapid pace and has a significant contribution to the Gross Domestic Product of India every year. . There is a great evolution of varied smartphones by different brands depending upon customer awareness and customer satisfaction. The new smartphones are featured with artificial intelligence, HD display, virtual assistant features like SIRI, Google assistant, etc., high-powered cameras, HD quality photos, Cloud storage feature, and better battery life. As per the study conducted by the mobile ecosystem forum, the highest penetration rate of smartphone users was in the age group of 16-35 years of old. The study of customer satisfaction is relevant for organizations to maintain long-term and healthy relationships with customers. In the presented thesis an effort has been made to understand the significant relationship between smartphone features and customers among Indian youth customers. An effort was also made to understand the significant relationship between the age, gender, income, and educational qualification of customers and awareness for the smartphone industry. It was finally concluded that smartphone features such as display, main camera, and touch battery life were the major contributing features in customer satisfaction (Indian young youth).

Word of Mouth, Customer Satisfaction, Smart Phones, Smart phone Features, Awareness, Perceived Image

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Stuti Jain, Shiv Singh Sarangdevot, "Factors Affecting Consumer Satisfaction among the Indian Young Youth Using Smart Phones: A Study," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.1024-1027, 2019.

Lately, we`ve seen a twist of online internet based totally business sites. It indicates an superb threat to share our surveys and evaluations for distinctive gadgets we purchase. Looking to the score cannot the simplest one assist a client to get an define about the object as an alternative the maximum perfect course is to peruse the audits about the item. Be that as it may, at that point a captivating issue comes up. Imagine a scenario in which the quantity of surveys is within the hundreds or hundreds. Which contain of 10 to 15 pages at that factor it is virtually no longer possible to experience each one of these surveys because of wastage of time and exertion. Here comes the importance of audits. To mine profitable information from audits to recognise a patron`s tendencies and make a precise cease pivotal. In this work, we recommend a sentiment based rating expectation technique to take care of this difficulty.

Energy efficient, Green city, Hybrid optimization, IoT, PSO, Raspberry Pi, WSN.information.

[1] Vijayan V P, Gopinathan E “Improving Network Coverage and Life-Time in a Cooperative Wireless mobile Sensor Network “ Fourth International Conference on Advances in computing and communications (ICACC) Aug, 2014. Published in IEEE Computer Society Proceedings. Print ISBN: 978-1-4799- 4364-7,INSPECAccessionNumber:14630874,DOI:10.1109/ICACC.2014.1 6 PP 42-45.
[2] N. Marchenko, T. Andre, G. Brandner, W. Masood, C. Bettstetter, “An Experimental Study of Selective Cooperative Relaying in Industrial Wireless Sensor Networks,” IEEE Trans. Industrial Informatics, vol. 10, no. 3, pp. 1806-1816, Aug. 2014.
[3] X. C. Liu; X. R. Gong, Y. Z. Zheng, “Reliable Cooperative Communications Based on Random Network Coding in Multi-Hop Relay WSNs,” IEEE Sensors Journal, vol. 14, no. 8, pp. 2514-2523, Aug. 2014.
[4] Juby Joseph, Vinodh P Vijayan” Misdirection Attack in WSN Due to Selfish Nodes; Detection and Suppression using Longer Path Protocol” International Journal of Advanced Research in Computer Science and Software Engineering, Volume 4, Issue 7, pp. 825-829, July 2014. ISSN: 2277 128X.
[5] X. Zhou, R. Zhang and C. K. Ho, “wireless information and power transfer: Architecture design and rate-energy tradeoff,” IEEE Trans. Wireless Commun., vol. 61, no. 11, pp. 4754-4767, Nov. 2013.
[6] V P Vijayan, Biju Paul “Multi Objective Traffic Prediction Using Type-2 Fuzzy Logic and Ambient Intelligence” International Conference on Advances in Computer Engineering 2010, Published in IEEE Computer Society Proceedings, ISBN: 978-0-7695-4058-0, Print ISBN: 978-1-4244-7154-6
[7] Vinodh P Vijayan, Deepti John, Merina Thomas, Neetha V Maliackal, Sara Sangeetha Varghese “Multi Agent Path Planning Approach to Dynamic Free Flight Environment”, International Journal of Recent Trends in Engineering (IJRTE), ISSN 1797-9617 Volume 1, Number 1, Page(s): 41-46, May 2009.

Vinodh P Vijayan, Neema George, Simy Mary Kurian, Sujitha M, "Improving Sensor Network in Sustainable City," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.1028-1032, 2019.

Humankind has put away in excess of 295 billion gigabytes (or 295 Exabyte) of information beginning around 1986, according to a report by the University of Southern California. Putting away and checking this information in generally disseminated conditions for all day, every day is an enormous errand for worldwide assistance associations. These datasets require high handling power which can`t be presented by conventional information bases as they are put away in an unstructured arrangement. Although one can utilize Map Reduce worldview to take care of this issue utilizing java-based Hadoop, it can`t give us with most extreme usefulness. Downsides can be defeated utilizing Hadoop-streaming methods that permit clients to characterize non-java executable for handling this dataset. This paper proposes a THESAURUS model which permits a quicker and more straightforward form of business examination.

Hadoop, MapReduce, HDFS, NoSQL

[1] Apache Hadoop.[Online].Available: http://hadoop.apache.org
[2] Apache Hadoop-Streaming.[Online].:http://hadoop- streaming.apache.org
[3] Cassandra wiki, operations. [Online]. Available: http://wiki.apache.org/cassandra/Operations
[4] NOSQL data storage [online]: http://nosql-database.org
[5] E. Dede, B. Sendir, P. Kuzlu, J. Weachock, M. Govindaraju, and L. Ramakrishnan, “A processing pipeline for cassandra datasets based on Hadoop streaming,” in Proc. IEEE Big Data Conf., Res. Track, Anchorage, AL, USA, pp. 168–175,2014.
[6] E. Dede, B. Sendir, P. Kuzlu, J. Weachock, M. Govindaraju, L. Ramakrishnan, "Processing Cassandra Datasets with Hadoop-Streaming Based Approaches",IEEE Transactions on Services Computing, Vol. 9,Issue 1,pp 46-58.
[7] J. Ekanayake, H. Li, B. Zhang, T. Gunarathne, S.-H. Bae,J. Qiu, and G. Fox, “Twister: A runtime for iterative mapreduce,” in Proc. 19th ACMInt. Symp. High Perform. Distrib. Comput., pp. 810–818,2010

Simy Mary Kurian, Neema George, Jinu P Sainudeen, Neethu Maria John, "Improved Analysis of Unstructured Datasets using Thesaurus Model," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.1033-1037, 2019.