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Social media are websites and computer programs that enable users to create and share information on the internet using a computer or a mobile phone. Large quantities of data are generated by social networks in seconds. The information which is generated in a social network is transformed into a flow by the subjects who produce, transmit, and consume it. This flow can be represented as a very complicated directional graph. In this graph each subject is represented as a node, and the flow of information is represented as a directed edge. In this paper, we introduce a method of dividing this complex directional graph by user and quantifying the flow of information between and among users based on information flow vectors. We propose a system that can monitor the flow of information in social networks using information flow vectors extracted from social media data. We also introduce an improved skyline algorithm that can respond quickly to a user’s various queries.

Information flow, Social media data, Skyline, Lambda architecture, MapReduce

[1] T. Hale. How Much Data Does the World Generate Every Minute? Accessed: Dec. 22, 2017.
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[5] M. Cha, F. Benevenuto, H. Haddadi, and K. Gummadi,‘‘The world of connection and Information flow in twitter’’ IEEE Trans. Syst., Man, Cybern.A, Syst., Humans, vol. 42, no.4pp.991-1998.
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[7] J. Scott, Social Network Analysis. Thousand Oaks, CA, USA: Sage, 2017.
[8] M. Kuramochi and G. Karypis, ‘‘An efficient algorithm for discovering frequent subgraphs,’’ IEEE Trans. Knowl. Data Eng., vol. 16, no. 9, pp. 1038–1051, Sep. 2004.
[9] X. Yan and J. Han, ‘‘gSpan: Graph-based substructure pattern mining,’’ in Proc. IEEE Int. Conf. Data Mining, Dec. 2002, pp. 721–724.
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[12] T. Lappas, K. Liu, and E. Terzi, ‘‘Finding a team of experts in social networks,’’ in Proc. 15th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2009, pp. 467–476.
[13] J. Xu and H. Chen, ‘‘Criminal network analysis and visualization,’’ Commun.ACM, vol. 48, no. 6, pp. 100–107, 2005.
[14] M. A. Bhuiyan and M. Al Hasan, ‘‘An iterative MapReduce based frequent subgraph mining algorithm,’’ IEEE Trans. Knowl. Data Eng., vol. 27, no. 3, pp. 608–620, Mar. 2015.
[15] A. Cuzzocrea, F. Jiang, and C. K. Leung, ‘‘Frequent subgraph mining from streams of linked graph structured data,’’ in Proc. EDBT/ICDT Workshops, 2015, pp. 237–244.
[16] R. Ahlswede, N. Cai, S.-Y. R. Li, and R. W. Yeung, ‘‘Network information flow,’’ IEEE Trans. Inf. Theory, vol. 46, no. 4, pp. 1204–1216, Jul. 2000.
[17] S. Borzsony, D. Kossmann, and K. Stocker, ‘‘The Skyline operator,’’ in Proc. 17th Int. Conf. Data Eng., 2001, pp. 421–430.
[18] J. Chomicki, P. Godfrey, J. Gryz, and D. Liang, ‘‘Skyline with presorting,’’ in Proc. 19th Int. Conf. Data Eng., 2003, pp. 717–719.
[19] J. Chomicki, P. Godfrey, J. Gryz, and D. Liang, ‘‘Skyline with presorting: Theory and optimizations,’’ in Proc. Intell. Inf. Process. Web Mining, 2005, pp. 595–604.
[20] I. Bartolini, P. Ciaccia, and M. Patella, ‘‘SaLSa: Computing the skyline without scanning the whole sky,’’ in Proc. 15th ACM Int. Conf. Inf. Knowl. Manage., 2006, pp. 405–414.
[21] A. Vlachou, C. Doulkeridis, and Y. Kotidis, ‘‘Angle-based space partitioning for efficient parallel skyline computation,’’ in Proc. Int. Conf. Manage. Data, 2008, pp. 227–238. 23826 VOLUME 6, 2018.

K.M. Jyothsna Priya, A. Srinivasulu, "A Survey on Information Flow Monitoring System Using Skyline Algorithm", International Journal of Computer Sciences and Engineering, Vol.07, Issue.06, pp.1-8, 2019.

Causation is one of the crucial relationships among the related variables that provide good stuff for data analytics. A causal relationship among a set of events exists when one or group of event/s is the result of the occurrence of the other event or a set of events. The primary objective of any research in data analytics or scientific analysis is to identify the level to which a relation exists among the subjective variables. Causal research can facilitate business environment to quantify the effect of present business practices on future production levels to aid in the business planning process. The process of discovering causal relationships among variables have multitude application areas like critical care services in medicine, advertising, bioinformatics, road safety ,share markets, and too more to be included. The present work targeted to study the existing methods of causal relationship discovery. The study also tried to propose the automated and straight forward causal relationship discovery methods which are scalable.

Decision tree, causal relationship,bayesian netorks,Structural Equation models,CDT

[1]. Alfieri’s C. F., et al. [C. F. Aliferis, A. Statnikov, I. Tsamardinos, S. Mani, and X. D.Koutsoukos, “Local causal and Markov blanket induction for causal discovery and feature selection for classification part I: Algorithms and empirical evaluation”, Journal of Machine Learning. Res., vol. 11, pp. 171–234, 2010.
[2]. Birch M.W.” The Detection of Partial Association”, Journal of Royal Statistical Society Aeries B (Methodological), Vol. 26. No. 2 (1964), pp. 313-324.
[3]. Bollen K.A., Pearl J. ”Eight Myths About Causality and Structural Equation Models. In: Morgan S.” (eds), Handbook of Causal Analysis for Social Research. Springer, Dordrecht (2013)
[4]. Christopher D. Ittner, “Strengthening causal inferences in positivist field studies”, Accounting, Organizations and Society 39 (2014) 545–549.
[5]. Constantin F. Aliferis, Alexander Statnikov, Ioannis Tsamardinos, Subramani Mani, Xenofon D. Koutsoukos, “Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification”, Journal of Machine Learning Research 11 (2010) 171-234.
[6]. Donald B. Rubin [Donald B. Rubin, “Estimating Causal Effects from Large Data Sets Using Propensity Scores” 15 October 1997 | Volume 127 Issue 8 Part 2 | Pages 757-763, Annals of Internal Medicine, American College of Physicians]
[7]. Frey L., D. Fisher, I. Tsamardinos, C. Aliferis, and A. Statnikov, “Identifying Markov blankets with decision tree induction,” in Proc. 3rd IEEE Int. Conf. Data Mining, Nov. 2003, pp. 59–66.]
[8]. Jin Z., J. Li, L. Liu, T. D. Le, B. Sun, and R. Wang, “Discovery of causal rules using partial association,” in Proc. IEEE 12th Int. Conf. Data Mining, Dec. 2012, pp. 309–318]
[9]. Jiuyong Li, et al [Jiuyong Li, Thuc Duy Le, Lin Liu, Jixue Liu, Zhou Jin, Bingyu Sun, and Saisai Ma”, From Observational Studies to Causal Rule Mining”. ACM Trans. Intell. Syst. Technol.2015
[10]. Jiuyong Li, Saisai Ma, Thuc Duy Le, Lin Liu and Jixue Liu “Causal Decision Trees,” arXiv: 1508.03812v1 [cs.AI] 16 Aug 2015]
[11]. Jiuyong Li, Lin Liu, Thuc Duy Le. Jiuyong Li • Lin Liu • Thuc Duy Le, “Practical Approaches to Causal Relationship Exploration” Jiuyong Li School of Information Technology and Mathematical Sciences University.2015]
[12]. Li. j, Liu. L, Le. T., “Practical approaches to causal relationship exploration”2015. X. 80 p. 55 illu., softcover, ISBN:978-3-319-14432-0, http://www.springer.com/978-3-319-14432-0]
[13]. Magliacane Sara, Tom Claassen, Joris M. Mooij, “Joint Causal Inference from Observational and Experimental Datasets”, Journal of Machine Learning Research, March 2017.
[14]. S. L. Morgan and D. J. Harding, “Matching estimators of causal effects: Prospects and pitfalls in theory and practice,” Sociological Methods Res., vol. 35, pp. 3–60, 2006.
[15]. Sander Greenland and Babette Brumback, “An overview of relations among causal modelling methods”,International Journal of Epidemiology, Volume 31, Issue 5, 1 October 2002, Pages 1030–1037.
[16]. Spirtes P., C. C. Glymour, and R. Scheines, Causation, Predication, and Search, 2nd ed. Cambridge, MA, USA: MIT Press, 2000.
[17]. Spirtes Peter. “Introduction to Causal Inference, ” Journal of Machine Learning Research 11 (2010) 1643-1662.
[18]. Stephen L. Morgan, David J. Harding, “Matching Estimators of Causal Effects”, Sociological Methods& Research Volume 35 Number 1August 2006 3-60 _ 2006 Sage Publications
[19]. Swati Hira and P. S. Deshpande ,“Mining precise cause and effect rules in large time series data of socio‑economic indicators,” SpringerPlus (2016) 5:1625.
[20]. Yeying Zhu, Donna L. Coffman and Debashis Ghosh, “A Boosting Algorithm for Estimating Generalized Propensity Scores with Continuous Treatments”, J. Causal Infer. 2015; 3(1): 25–40.

S. Sajida, M. Padmavathamma, "A Study on Scalable and automated Causal Relationship Discovery Strategies In Data Analytics", International Journal of Computer Sciences and Engineering, Vol.07, Issue.06, pp.9-15, 2019.

The Internet and digitization have already turned many elements upside down. The financial sector is no exception. Today, a new epoch of financial service, called “FinTech,” has emerged. Blockchain, an innovation by FinTech, has attracted considerable attention. Blockchain is a relatively new technology that has shown a lot of possibilities. It emerged in 2009 as a public ledger of all bitcoin transactions. It became more popular since it can be used as backbone for various applications in finance, media, smart property, smart healthcare, security, governmental services and many more. Motivated by the recent explosion of interest around Blockchains, we examined various blockchain’s applications. This paper discusses about Blockchain characteristics and also presents its limitations. This work even explores the types of fraud and malicious activities that can be prevented by Blockchain technology and identifies attacks to which Blockchain remains vulnerable. Here we also discuss about using Blockchain to Avert Online Attacks.

FinTech, Digitalization, Blockchain, Fraud detection, Hacking prevention, Online attacks

[1]. Dorri, A., Kanhere, S.S., Jurdak, R.: Blockchain in internet of things: challenges and solutions. arXiv preprint arXiv:1608.05187 (2016)
[2]. Christidis, K., Devetsikiotis, M.: Blockchains and smart contracts for the Internet of Things. IEEE Access 4, 2292–2303 (2016)
[3]. Lin, I.C., Liao, T.C.: A survey of Blockchain security issues and challenges. Int. J. Netw. Secur. 19(5), 653–659 (2017). https://doi.org/10.6633/ijns.201709.19(5).01
[4]. Zheng, Z, Xie, S., Dai, HN., Chen, X., Wang, H.: An overview of Blockchain technology: architecture, consensus, and future Trends. In: 978-1-5386-1996-4/17 6th International Congress on Big Data PP557-564 IEEE (2017).
[5]. Singh, S, Singh, N.: Blockchain: future of financial and cyber security. In: 978-1-5090-5256-1/16/PP463-467 IEEE (2016)
[6]. Porru, S., Pinna, A., Marchesi, M., Tonelli, R.: Blockchain-oriented software engineering: challenges and new directions. In: 39th IEEE International Conference on Software Engineering Companion PP169-179 IEEE/ACM (2017)
[7]. Hou, H.: The application of Blockchain technology in E-government in China. In: 978-1-5090-2991-4/17/IEEE (2017)
[8]. Li, Y, Huang, J., Qin, S., Wang, R.: Big data model of security sharing based on Blockchain. In: 3rd International Conference on Big Data Computing and Communications 978-1-5386-3349-6/17, pp. 117–121 IEEE (2017).
[9]. Zikratov, I., Kuzmin, A., Akimenko, V., Niculichev, V., Yalansky, L.: Ensuring data integrity using Blockchain technology. In: Proceeding of the 20th Conference of fruct Association ISSN 2305-7254 IEEE (2017)

Vempalli R Durgeswar, C.C. Kalyan Srinivas, "Blockchain in FinTech: Applications, and Limitations", International Journal of Computer Sciences and Engineering, Vol.07, Issue.06, pp.16-19, 2019.

In recent decades, the world has been witnessing unprecedented growth of urban population. This trend is likely to continue in next decades, which may result in unusual sizes and densities of cities. This leads to social, economical and environmental challenges. Thus, smarter solutions are needed to face these challenges, and to provide better services to the dwellers - by ensuring efficient and optimal utilization of available resources. The emerging technologies like Internet of Things, Cloud Computing, Big Data, advances wireless technologies and smart devices can deliver innovative solutions to the citizens, and lead to the emergence of what is called the “smart cities”. Such fusion of technologies aims to improve overall quality of life in many ways. This paper presents state-of-the-art technologies that are shaping smart cities. Further the paper discusses the challenges that must be addressed to, so that the smart cities become safer, and sustainable.

Smart Cities, Urban Development, Internet of Things, Cloud Computing, Sustainability, Blockchain

[1] Yogita Pundir, Nancy Sharma and Yaduvir Singh, “Internet of Things(IoT): Challenges and Future Directions.” International Journal of Advanced Research in Computer and Communication Engineering Vol. 5, Issue 3, March 2016.
[2] Smart Cities India 2015 Brochure,”Smarter Solutions for a Better Tomorrow” Pragati Maidan, New Delhi 20th -22th May, 2015.
[3] Ali Dorri, Salil S. Kanhere, and Raja Jurdak, ”Blockchain in Internet of Things : Challenges and Solutions”
[4] Kamanashis Biswas Vallipuram Muthukkumarasamy, “Securing Smart Cities Using Blockchain Technology”, 2016 IEEE 18th International Conference on High Performance Computing and Communications; IEEE 14th International Conference on Smart City; IEEE 2nd International Conference on Data Science and Systems

Murali Mohan Kotha, "Technologies That Shape Smart Cities", International Journal of Computer Sciences and Engineering, Vol.07, Issue.06, pp.20-23, 2019.

The weather forecasting has been prepared using the atmosphere condition manually. However, these estimations are unstable and imprecise for long duration. The machine learning is more robust compute the weather forecasting with precise prediction for long duration. This paper has proposed the Artificial Neural Networks (ANN) based model with supervised learning model in weather prediction. This proposed prototype is designed to predict different weather conditions with linear regression. This model is simulated with different dataset using supervised learning machine learning data repository. The proposed model performed better than traditional method in weather prediction. The atmosphere parameters such as temperature, pressure, dew, humidity are exploited to design, train and test a model. The future climate is predicted using machine learning by analyzing the parameters.

Weather, Neural Network, Climate, Forecast, Linear Regression, Machine Learning

[1]. Mark Holmstrom, Dylan Liu, Christopher Vo, “Machine Learning Applied to Weather Forecasting Stanford University, 2016.
[2]. PiyushKapoor and Sarabjeet Singh Bedi “Weather Forecasting Using Sliding Window Algorithm”, Kvantum Inc., Gurgaon, India.
[3]. Anu B. Nair, R. Umamaheswari, P. Kuppusamy, “A Machine Learning-based State-of-theart Approach to Identifying the Person behind an E-mail ID”, International Journal of Computer Applications, Vol. 136, No.5, pp. 1-4, Feb 2016.
[4]. DivyaChauhan, Jawahar Thakur “Data mining Techniques for Weather Prediction: A Review, 2013
[5]. Qing Yi Feng, RuggeroVasile, Marc Segond, AviGozolchiani, Yang Wang, Markus Abel,
ShilomoHavlin, Armin Bunde, and Henk A. Dijkstra, “ ClimateLearn: A machine-learning approach for climate prediction using network measures”, Germany, 2016.
[6]. Kuppusamy. P, “Smart Home Automation Using Sensors and Internet of Things”, Asian Journal of Research in Social Sciences and Humanities, Vol. 6, Issue 8, pp. 2642-2649, Aug 2016.
[7]. Siddharth S. Bhatkande ,Roopa G. Hubballi “Weather Prediction Based on Decision Tree Algorithm Using Data Mining Techniques”, International Journal of Advanced Research in Computer and Communication Engineering, 2016.
[8]. Sanyam Gupta, Indumathy, GovindSinghal, “Weather Prediction Using Normal Equation Method and Linear Regression Techniques”, International Journal of Computer Science and Information Technologies, 2016.
[9]. Muthulakshmi A, BaghavathiPriya, S, “A survey on weather forecasting to predict rainfall using big data analytics”, IJISET, 2015.
[10]. Aditya Grover, AshishKapoor, Eric Horvitz (n.d.) “A Deep Hybrid Model for Weather Forecasting”, Microsoft Research, Redmond.
[11]. John K. Williams and D. A. Ahijevych, C. J. Kessinger, T. R. Saxen, M. Steiner and S. Dettling “A Machine Learning Approach to Finding Weather Regimes and Skillful Predictor Combinations for Short-Term Storm Forecasting”, National Center for Atmospheric Research, Boulder, Colorado.
[12]. Kuppusamy, P, Kalpana, R, Venkateswara Rao, P. V. “Optimized traffic control and data processing using IoT”, Springer Cluster Computing, pp. 1-10, February 2018.
[13]. Maqsood, I, Khan, MR, Abraham, A. "An ensemble of neural networks for weather forecasting," Neural Comput. & Applic., Vol. 13,2004.
[14]. Mohsen Hayati, and Zahra Mohebi, "Application of Artificial Neural Networks for Temperature Forecasting," International Journal of Engineering and Applied Sciences, Vol.4 No.3, 2008, pp:164-168.

P. Kuppusamy, K. Jayalakshmi, C. Himavathi, "Application of Machine Learning for Weather Forecasting Using Artificial Neural Networks", International Journal of Computer Sciences and Engineering, Vol.07, Issue.06, pp.24-27, 2019.

In the future most of market deals with the Blockchain Technology, because this technology has decentralized database that records the data such as finance contracts, physical assets, and supply chain information. It has achievement over the time and is currently dominated and used by Bitcoin. It is not a new technology but it shown up with the bitcoin, now a days some of the transactions are done with the cryptocurrency. It gives innovative solutions for FinTech Industry. This technology has energized the financial services industry globally. This concept already brought a disruption in the financial industry. Coming the some of the Industries, which already implementing this technology are, Deutsche Bank, DBS Bank, EBA (Euro Banking Association), US Federal Reserve are some of Financial Sector Banks are going to use this Technology in as like Digital Payments, Assets. This technology is Transparent Business way. So this is very secured and transparent. It is like as distributed ledger.

Cryptocurrency, blockchain, distributed ledger, digital payments

[1] S. Nakamoto, “Bitcoin: A peer-to-peer electronic cash system,” 2008.
[2] Q. Lin, P. Chang, G. Chen, B. C. Ooi, K. Tan, and Z. Wang, “Towards a non-2pc transaction management in distributed database systems,” in Proceedings of ACM International Conference on Management of Data (SIGMOD), San Francisco, CA, USA, 2016, pp. 1659– 1674.Management of Data (SIGMOD), San Francisco, CA, USA, 2016, pp. 1659– 1674.
[3] Sachin M. Kolekar, Rahul P. More, Smita S. Bachal, Anuradha V. Yenkikar, “Review Paper on Untwist Blockchain: A Data Handling Process of Blockchain Systems”, International Conference of information, Communication, Engineering and Technology (ICICET).
[4] Stuart Haber, W. Scott Stronetta, Bellore, “How to Time-Stamp a Digital Document”.
[5] Mildred Chidinma Okoye “New Applications of blockchain technology to voting and lending”.
[6] A. Shanti Bruyn, Research Paper, “Blockchain an introduction”.

Kambala Prudhviraj, K. Venkataramana, "Study of Block Chain Technology and its Applications", International Journal of Computer Sciences and Engineering, Vol.07, Issue.06, pp.28-31, 2019.

Advanced digital video compression technologies have become an essential part of motion estimation strategy. The motion estimation of H.264/AVC is a tedious procedure. Numerous calculations were proposed for the video stream motion estimation, however they have a high computational multifaceted nature which altogether expands the encoder unpredictability. Video compression encoding computerized video to take up less storage space and transmission data transfer capacity. This paper proposes the Modified Unsymmetrical-Cross Multi Hexagon-Grid Search (MUMHexagonS) algorithm is utilized for motion estimation searching process .The conventional search patterns are replaced by the Asymmetrical Improved Cross Search Pattern, Minimized square search and Improved Diamond Search Pattern. The proposed motion estimation and video compression system lessen the movement estimation time, compression time, calculation cost and it accomplishes the better encoding effectiveness and great video quality.

Video compression, Unsymmetrical Cross Multi Hexagon Search (UMHexagonS),Motion Estimation, Improved Diamond Search

[1] Koga, T, Iinuma, K, Hirano, A, Iijima, Y & Ishiguro, T 1981, ‘Motion compensated inter frame coding for video conferencing’, In proceedings of National Tele communication Conference, New Orleans, LA, pp. G5.3.1–G5.3.5.
[2] R. Li, B. Zeng, and M. L. Liou, “A new three-step search algorithm for block motion estimation,” IEEE Trans. Circuits Syst. Video Technol., vol. 4, pp. 438–443, Aug. 1994.
[3] Akash Ambulkar, Shailesh Wankhede, Sujit Kautkar, Mukesh Angrakh & Vidya N More 2012, ‘Enhanced Directional Hexagonal Search Algorithm’, in proceedings of 2012 IEEE International Conference on Circuits and Systems (ICCAS), pp. 143-146.
[4] Jonathan Fabrizio, Severine Dubuisson & Dominique Bereziat 2012, ‘Motion compensation based on Tangent Distance prediction for video compression’, Signal Processing: Image Communication, vol. 27,
no. 2, pp. 153-171.
[5] Anmin Liu, Weisi Lin, Manoranjan Paul, Fan Zhang & Chenwei Deng 2011, ‘Optimal Compression Plane for Efficient Video Coding’, IEEE Transactions on Image Processing, vol. 20, no. 10, pp. 2788-2799.
[6] Vo, DT & Nguyen, TQ 2008, ‘Quality Enhancement for Motion JPEG Using Temporal Redundancies’, IEEE Trans. Circuits Syst. Video Technol., vol. 18, no. 5, pp. 609-619.
[7] Wei Chena, Yonghong Tiana, Yaowei Wang & Tiejun Huang 2016, ‘Fixed-point Gaussian Mixture Model for analysis-friendly surveillance video coding’, Computer Vision and Image Understanding, vol. 142, pp. 65-79.
[8] Hossein Bisjerdi Mohammad & Alireza Behrad 2012, ‘Video Compression using a New Active Mesh Based Motion Compensation Algorithm in Wavelet Sub-Bands’, Journal of Signal and Information Processing, vol. 3, pp. 368-376.
[9] Hossein Bisjerdi Mohammad & Alireza Behrad 2012, ‘Video Compression using a New Active Mesh Based Motion Compensation Algorithm in Wavelet Sub-Bands’, Journal of Signal and Information Processing, vol. 3, pp. 368-376.
[10] Ashwin, S, Jayanthi Sree, S & Aravind Kumar, S 2013, ‘Study of the Contemporary Motion Estimation Techniques for Video Coding’, International Journal of Recent Technology and Engineering (IJRTE), vol. 2, no. 1, pp. 2277-3878.
[11] Pengyu Liu & Kebin Jia 2013, ‘A Motion Characteristics Based Unsymmetrical Cross Multihexagon Grid Search Algorithm for Fast Motion Estimation’, Information Technology, vol. 12, no. 15.
[12] Dengyin,Yang,2018, ‘Distributed Compressive Video Sensing with Adaptive Reconstruction Based on Temporal Corellation’, In proceedings of IEEE Third International conference on Image, Vision and Computing,June-2018.

R.Sudhakar, P.Kuppusamy, P.V.Venkateswara Rao, "Fast Motion Estimation using Modified Unsymmetrical Cross MultiHexagon Grid Search Algorithms for Video Coding Techniques", International Journal of Computer Sciences and Engineering, Vol.07, Issue.06, pp.32-36, 2019.

In Data sources for information feed into a Big Data achievement as you might expect contain sensitive or confined information or key logical property along with non-sensitive data. In the Big data world securing the sensitive data be renewed into more intricate and time overwhelming process. In the big data distribution of sensitive, it exacerbates the threat of sensitive data falling into the un-authorized. To battle this sensitive data threat, enterprises turn to cryptosystem. In the cryptosystem encryption is the process of encoding sensitive data so that only authorized or privileged parties can decrypt and read the sensitive data applying this methodology in application level we provide complete security on the sensitive data.

Cryptography – Policy – Data Encryption - Privileged User – Enhanced Schmidt Samoa

[1]. http://www.sas.com/en_us/insights/big-data/what-is-big-data.html
[2]. https://globalecco.org/big-data-insider-threats-and-international-intelligence-sharing
[3]. "Sensitive Information" (definition) Aug. 23, 1996. Retrieved Feb. 9 2013.
[4]. "DEPARTMENT OF INDUSTRY: PERSONAL INFORMATION PROTECTION AND ELECTRONIC DOCUMENTS ACT" Canada Gazette, Apr. 03 2002. Retrieved Feb. 9 2013.
[5]. http://motherboard.vice.com/read/even-tor-cant-save-small-time-hackers
[6]. https://www.qubole.com/blog/big-data/hadoop-security-issues/
[7]. https://securosis.com/assets/library/reports/Securing_Hadoop_Final_V2.pdf
[8]. https://securosis.com/blog/securing-hadoop-architectural-security-issues
[9]. http://www.bmc.com/blogs/big-data-security-issues-challenges-for-2016/
[10]. https://en.wikipedia.org/wiki/Health_Insurance_Portability_and_Accountability_Act
[11]. http://searchdatamanagement.techtarget.com/definition/HIPAA
[12]. http://blog.vormetric.com/2015/06/23/locking-down-data-full-disk-encryption-vs-file-level-encryption/
[13]. Performance analysis of Jordan Totient RSA (JkRSA) and NTRU, International Journal of Scientific & Engineering Research, Volume 5, Issue 3, March-2014 1099 ISSN 2229-5518
[14]. https://www.vormetric.com/data-security-solutions/use-cases/privileged-user

Narayana Galla, Padmavathamma Mokkala, "An Enhanced Schmidt Samoa Cryptosystem for Securing Health Care information in Big Data Scenario", International Journal of Computer Sciences and Engineering, Vol.07, Issue.06, pp.37-40, 2019.

One of the foremost active areas of inferring structure and principles of biological datasets is that the use of knowledge mining to resolve biological issues. Some typical samples of biological analysis performed by data processing involve supermolecule structure prediction; cistron Classification, analysis of mutations in cancer and cistron expressions. Over recent years the studies in proteomic, genetics Associate in Nursing the varied different biological researches has generated a progressively great deal of biological knowledge. Drawing conclusions from this knowledge needs subtle machine analysis so as to interpret the information. As biological data and research become ever vaster, it is important that the application of data mining progresses in order to continue the development of an active area of research within Bioinformatics. This aims to draw information from varied academic sources in order to discuss an overview of data mining, Bioinformatics, the application of data mining in Bioinformatics and a conclusive summary.

Data Mining, Data Mining Techniques, Bioinformatics

[1]. Data Mining For Bioinformatic Applications, Zengyou Heengyou, ISBN: 978-0-08-100100-4 2015.
[2]. Application Of Data Mining In Bioinformatics, Khalid Raza, Indian Journal Of Computer Science And Engineering Vol 1 No 2, 114-118, ISSN : 0976-5166
[3]. Data Mining in Bioinformatics (BIOKDD), Mohammed J Zaki, 1 George Karypis,2 and Jiong Yang3, Published online 2007 Apr 11. doi: 10.1186/1748-7188-2-4
[4]. An Introduction Into Data Mining In Bioinformatics, Ryan Littlefield, Apr 11,2017
[5]. Comparative Analysis Of Data Mining Techniques On Education Dataset, Sumit Garg, Arvind K. Sharma, International Journal Of Computer Applications(0975-8887), Vol 74- No.5, July 2013
[6]. Jiawei Han and Micheline Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, CA, 2005.
[7]. Exploiting Data Mining Techniques For Improving The Efficiency Of Time Series Data Using Spss-Clementine, Pushpalata Pujari, Jyoti Bala Gupta, Researchers World- Journal Of Arts, Science & Commerce ■ E-ISSN 2229-4686 ■ ISSN 2231-4172
[8]. Bioinformatics methods to predict protein structure and function. A practical approach, Edwards YJ1, Cottage A., Mol Biotechnol. 2003 Feb;23(2):139-66. https://www.ncbi.nlm.nih.gov/pubmed/12632698

O. Yamini, S. Ramakrishna, "Bioinformatics: An Application of Data Mining", International Journal of Computer Sciences and Engineering, Vol.07, Issue.06, pp.41-45, 2019.

The theme of the work done is to have an SMS text-enabled communication medium between the outsider and the guardian (through GSM communication). At the moment there are many wearables in the market which help track the daily activity of people and also help find the person using Wi-Fi and Bluetooth services present on the device. However, Wi-Fi and Bluetooth appear to be an unreliable medium of communication between the guardian and the person. The guardian can send text with specific keywords like “LOCATION”, “TEMPERATURE”, “HEARTBEAT”, “SOS”, “BUZZER”, and the wearable device will reply you back with a text containing the health conditions and also accurate real-time location of the person which upon tapping will provide directions to the person’s position on Google Maps App and will also give the surroundings temperature. So our project mainly focuses on the basic design and implementation of such devices.The secondary measure taken is the implementation of the bright SOS light and the distress alarm buzzer present on such devices which when activated by the guardian via SMS text displays SOS light brightly and sound an alarm which a bystander can easily spot as a sign of distress.

IOT, Safety, Wearable, GSM, GPS, Sensors

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Sri Shanthi Meesala, G.V. Ramesh Babu, "Sensor Based Healthcare Model", International Journal of Computer Sciences and Engineering, Vol.07, Issue.06, pp.46-50, 2019.