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Diabetes is a dreadful disorder facing the mankind from children to the older people irrespective of their age. This paper gives a mathematical model and analytics of different types of the diabetic population with different types medical managements. Diabetes is mainly a pancreatic disorder where there is insufficient secretion of insulin or improper functioning in the utilization of insulin for Glucose metabolism. Mainly there are five types of diabetes disorders such as Type-1, Type-2, Gestational diabetes, Juvenile diabetes and, MODY diabetes. In the sample population taken for study, these various types of the pancreatic disorders can be brought under control by medical managements like Allopathy, Siddha, Homeopathy and Ayurvedic treatments. The paper statistically analyses the diabetic population for number of patients under controls in various types of medical management by a mathematical model. Null hypothesis assumes that the numbers of paients under control of diabetes in various managements are independent of the type of treatments given by allopathy and other alternative methods. The powerful statistical tool ANOVA finds if there is significant difference between class means in view of variability within the separate classes. ANOVA method is used to analyze the sample population and the null hypothesis assumes that the number of patients with different level of controls is the same for different types of treatments in the sample diabetic population. The calculated value of variance ratio F is > the table value at 5% level. So the null hypothesis may be rejected at 5% level of significance. It gives an inference that there is significant difference between treatments given by various methods like allopathy, siddha, ayurvedic, homeopathy in the level of control for patients. Even though diabetes can never be cured by any method but can be kept under control to avoid complications in major organs.

MODY, STATISTICAL MODELING, Clinical Predictions

[1]. Web-based Fuzzy Expert System for Diabetes Diagnosis I.K. Mujawar1*, B.T. Jadhav2 , International Journal of Computer Sciences and Engineering OVol.-7, Issue-2, Feb 2019
[2]. Mirza Shuja1*, Sonu Mittal2, Majid Zaman3 1, 2 School of Computer and System Sciences, International Journal of Computer Sciences and Engineering Open Access Survey Paper Vol.-7, Issue-1, Jan 2019 E-ISSN: 2347-2693.
[3]. K. Saravanapriya* , J. Bagyamani ,”Performance Analysis of Classification Algorithms on Diabetes Dataset”International Journal of Computer Sciences and Engineering Open Access Research Paper Volume-5, Issue-9 E-ISSN: 2347-2693
[4]. An Overview of the Studies of Health Information Systems in Turkey” by O. Sebetci and M. Aksel International Journal of Computer Sciences and Engineering Open Access Review Paper Volume-4, Issue-8.
[5]. (www.nlm.nih.gov/medlineplus/healthtopics.html.and(www. niddk.nih.gov)
[6]. Centers for Disease Control and Prevention. National diabetes statistics report, 2017. Centers or Disease Control and Prevention website.
[7]. Analysis Of Variance - ANOVA https:// www. investopedia.com/terms/a/anova.asp#ixzz54QxqcbsQ
[8]. www.cdc.gov/diabetes/pdfs/data/statistics/national-diabetes-statistics-report.pdf (PDF, 1.3 MB).

R. Jamuna, "Analytics by Anova in Clinical Predictions," International Journal of Computer Sciences and Engineering, Vol.7, Issue.9, pp.167-170, 2019.

A micro-grid approach plays an enormous role in the increased penetration of renewable energy resource into grid thus reducing the emissions due to large coal fired power plants. The energy management in micro-grid is a challenging task as a major share of the generation is from Renewable Energy Sources. Usually, there are Power electronic interfaces through which the local generators are connected to the micro-grid which enables the control capabilities such as generation-demand management through active reactive power control, synchronization of the inverter to grid, meeting the power quality standards for the injected currents, maximum power point tracking etc. The active-reactive power delivered by the inverter is controlled by current control with the objective of the steady state and transient state performance requirements.

PV Array, Wind Power, Micro-grid

[1] EPIA, “Global market outlook for photovoltaics until 2016,” EPIA Report, May (2012)
[2] J. H. R. Enslin, “The role of power electronics and storage to increase penetration levels of renewable power,” Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century, IEEE Press, pp. 1-2, July (2008) DOI: 10.1109/PES.2008.4596958
[3] O. Edenhofer, R. Pichs-Madruga and Y. Sokona, Eds., “Renewable energy sources and climate change mitigation,” SRREN_FD_SPN_Final Report, http:/ /www.ipcc.ch/report/srren/, (2012)
[4] A. Luque and S. Hegedus, Ed., Handbook of photovoltaic science and engineering (Wiley, Chichester, 2003)
[5] L. Freris, D. Infield, Renewable energy in power systems (Wiley, Chichester, 2008) [first edition]
[6] B. Parida, S. Iniyan, and R. Goic, “A review of solar photovoltaic technologies” Renewable and Sustainable Energy Review, 15, 1625-1636 (2011) DOI: 10.1016/j.rser.2010.11.032
[7] S. Kurtz, “Opportunities and challenges for development of a mature concentrating photovoltaic power industry,” Technical Report NREL/TP-520-43208, Revised November (2009)
[8] EPTP, “A strategic research agenda for photovoltaic solar energy technology,” September (2011) [second edition]
[9] [10] J. Karp, “Concentrating solar power: progress and trends,” Jacobs School of Engineering, University of California San Diego, Triton SPIE/OSA, February 12 (2009)
[10] L. Micheli, N. Sarmah, X. Luo, K. S. Reddy, and T. K. Mallick, “Opportunities and challenges in micro- and nano-technologies for concentrating photovoltaic cooling: A review” Renewable and Sustainable Energy Reviews, 20, 595-610 (2013) DOI: 10.1016/j.rser.2012.11.051
[11] H. Zhang and L. L. Lai, “Research on wind and solar penetration in a 9- bus network” Proc. IEEE Power and Energy Society General Meeting, IEEE Press, pp. 1-6, July (2012) DOI: 10.1109/PESGM.2012.6345218
[12] P. Komor, “Wind and solar electricity: challenges and opportunities,” University of Colorado at Boulder, Pew Center on Global Climate Change, June (2009)
[13] GWEC, “Global wind report annual market update 2012,” http://www.gwec.net/wpcontent/uploads/2012/06/Annual_report_2012_LowRes.pdf, GWEC Report, April (2013)
[14] K. Grogg, “Harvesting the wind: the physics of wind turbines,” Carleton College, Physics and Astronomy Comps Papers 2005,mmhttp://digitalcommons. carleton.edu/pacp/7, April 13 (2005)
[15] B. S. Borowy and Z. M. Salameh, “Methodology for optimally sizing the combination of a battery bank and PV array in a Wind/PV hybrid system” IEEE Transactions on Energy Conversion, 11, 367-375 (1996) DOI: 10.1109/60.507648
[16] W. Kellogg, G. Venkataramanan, and V. Gerez, “Optimal unit sizing for a hybrid wind-photovoltaic generating system” Electric Power Systems Research, 39, 35-38 (1996) DOI: 10.1016/S0378-7796(96)01096-6
[17] R. Chedid and Y. Saliba, “Optimization and control of autonomous renewable energy systems” International Journal Of Energy Research, 20, 609-624 (1996) DOI: 10.1002/(SICI)1099-114X(199607)20:7<609::AID-ER176>3.0.CO;2-O
[18] S. H. Karaki, I. R. B. Chedid, and R. Ramadan, “Probabilistic performance assessment of autonomous solar-wind energy conversion systems” IEEE Transactions on Energy Conversion, 14, 766-772 (1999) DOI: 10.1109/60.790949
[19] R. Luna-Rubio, M. Trejo-Perea, D. Vargas-Vázquez, and G. J. Ríos-Moreno, “Optimal sizing of renewable hybrids energy systems: A review of methodologies” Solar Energy, 86, 1077-1088 (2012) DOI: 10.1016/j.solener.2011.10.016
[20] [21] P. G. Arul, V. K. Ramachandaramurthy, and R. K. Rajkumar, “Control strategies for a hybrid renewable energy system: A review” Renewable and Sustainable Energy Reviews, 42, 597-608 (2015) DOI: 10.1016/j.rser.2014.10.062
[21] B. Bhandari, K. T. Lee, G. Y. Lee, Y. M. Cho, and S. H. Ahn, “Optimization of hybrid renewable energy power systems: A review” International Journal of Precison Engineering and Manufacturing - Green Technology, 2, 99-112 (2015) DOI: 10.1007/s40684-015-0013-z
[22] M. Esteban, Q. Zhang, A. Utama, T. Tezuka, and K. N. Ishihara, “Methodology to estimate the output of a dual solar-wind renewable energy system in Japan” Energy Policy, 38, 7793-7802 (2010) DOI: 10.1016/j.enpol.2010.08.039
[23] H. H. Chen, H. Y. Kang, and A. H. I. Lee, “Strategic selection of suitable projects for hybrid solar-wind power generation systems” Renewable and Sustainable Energy Reviews, 14, 413-421 (2010) DOI: 10.1016/j.rser.2009.08.004
[24] R. Dufo-López, J. L. Bernal-Agustín, and F. Mendoza, “Design and economic analysis of hybrid PV-wind systems connected to the grid for the intermittent production of hydrogen” Energy Policy, 37, 3082-3095 (2009) DOI: 10.1016/j.enpol.2009.03.059

Yogita Shakywar, Shiv Tripathi, "Review of Control and Modelling Assessment of grid Connected Micro Grid," International Journal of Computer Sciences and Engineering, Vol.7, Issue.9, pp.171-175, 2019.

In the hierarchical control of an islanded microgrid, secondary control could be centralized or distributed. The former control strategy has several disadvantages, such as single point of failure at the level of the central controller as well as high investment of communication infrastructure. In this paper three-layer architecture of distributed control. The agent layer is a multi-agent system in which each agent is in charge of a distributed generation unit. Due to communication network constraints, agents are connected only to nearby neighbors. However, by using consensus algorithms the agents can discover the required global information and compute new references for the control layer. In this paper, a review of distributed control approaches for power quality improvement is presented which encompasses harmonic compensation, loss mitigation and optimum power sharing in multi-source-load distributed power network.

Distributed Control, Real-time Simulation, Micro-grid

[1] N. Hatziargyriou, Microgrids: Architectures and Control. 2014.
[2] D. E. Olivares, A. Mehrizi-Sani, A. H. Etemadi, C. A. Cañizares, R. Iravani, M. Kazerani, A. H. Hajimiragha, O. Gomis-Bellmunt, M. Saeedifard, R. Palma-Behnke, G. A. Jiménez-Estévez, and N. D. Hatziargyriou, “Trends in microgrid control,” IEEE Trans. Smart Grid, vol. 5, no. 4, pp. 1905–1919, 2014.
[3] J. M. Guerrero, M. Chandorkar, T. Lee, and P. C. Loh, “Advanced Control Architectures for Intelligent Microgrids; Part I: Decentralized and Hierarchical Control,” Ind. Electron. IEEE Trans., vol. 60, no. 4, pp. 1254–1262, 2013.
[4] M. Yazdanian, A. Mehrizi-sani, G. S. Member, and A. Mehrizi-sani, “Distributed Control Techniques in Microgrids,” Smart Grid, IEEE Trans., vol. 5, no. 6, pp. 2901–2909, 2014.
[5] S. D. J. D. J. McArthur, E. M. M. Davidson, V. M. M. Catterson, A. L. L. Dimeas, N. D. D. Hatziargyriou, F. Ponci, and T. Funabashi, “Multi-Agent Systems for Power Engineering Applications-Part I: Concepts, Approaches, and Technical Challenges,” IEEE Trans. Power Syst., vol. 22, no. 4, pp. 1743–1752, 2007.
[6] W. Liu, W. Gu, W. Sheng, X. Meng, Z. Wu, and W. Chen, “Decentralized multi-agent system-based cooperative frequency control for autonomous microgrids with communication constraints,” IEEE Trans. Sustain. Energy, vol. 5, no. 2, pp. 446–456, 2014.
[7] Q. Li, F. Chen, M. Chen, J. M. Guerrero, and D. Abbott, “Agent-Based Decentralized Control Method for Islanded Microgrids,” IEEE Trans. Smart Grid, vol. 7, no. 2, pp. 637–649, 2016.
[8] J. Rocabert, A. Luna, F. Blaabjerg, and I. Paper, “Control of Power Converters in AC Microgrids.pdf,” IEEE Trans. Power Electron., vol. 27, no. 11, pp. 4734–4749, 2012.
[9] R. Olfati-Saber, J. A. Fax, and R. M. Murray, “Consensus and cooperation in networked multi-agent systems,” Proc. IEEE, vol. 95, no. 1, pp. 215–233, 2007.
[10] A. H. Sayed, “Adaptive networks,” Proc. IEEE, vol. 102, no. 4, pp. 460–497, 2014.
[11] S. Scherfke, “aiomas’ documentation.” [Online]. Available: https://aiomas.readthedocs.io/en/latest/.

Manish Kanathe, Shiv Tripathi, "Review of Investigation on Distributed Control of Islanded Micro Grid," International Journal of Computer Sciences and Engineering, Vol.7, Issue.9, pp.176-180, 2019.

In this paper, the review of the multiple input multiple output using space time block code on IEEE 802.16 system. The Worldwide Interoperability for Microwaves Access technology which can offer high speed voice, image, and video and data service up to base on standard 802-16 wireless MAN is configured in the same way as a traditional cellular network. The range of WiMAX makes the system very attractive for users, but there will be slightly higher BER at low SNR. In this paper the study of different types of 4G and 5G technique and explain the advantage and disadvantage of the system.

WiMAX, OFDM, Rayleigh Channel, MIMO-OFDM, BER

[1] Akhilesh Venkatasubramanian, Krithika. V and Partibane. B, “Channel Estimation For A Multi-User MIMOOFDM- IDMA System”, International Conference on Communication and Signal Processing, April 6-8, 2017, India.
[2] R. Prasad, C. R. Murthy, B. D. Rao, "Joint channel estimation and data detection in MIMO-OFDM systems: A sparse Bayesian learning approach", IEEE Trans. Signal Process., vol. 63, no. 20, pp. 5369-5382, Oct. 2015.
[3] R. Prasad, C. R. Murthy, B. D. Rao, "Joint approximately sparse channel estimation and data detection in OFDM systems using sparse Bayesian learning", IEEE Trans. Signal Process., vol. 62, no. 14, pp. 3591-3603, Jul. 2014.
[4] Mel Li, Xiang Wang and Kun Zhang, “Comparative Study of Adaptive Filter Channel Estimation Technique in MIMO-OFDM System Based on STBC”, Proceedings of the 2014 International Conference on Machine Learning and Cybernetics, Lanzhou, 13-16 July, 2014.
[5] Muhammet Nuri Seyman and Necmi Taspmar, "Channel estimation based on neural network in space time block coded MIMO-OFDM system", Digital Signal Processing, Vol. 23, No.1, pp. 275-280, Jan. 2013.
[6] Biswajit Sahoo, Ravi Ranjan Prasad, and P. Samundiswary, “BER Analysis of Mobile WiMAX System using LDPC Coding and MIMO System under Rayleigh Channel”, International conference on Communication and Signal Processing, April 3-5, 2013, India.
[7] Li Mei, Wang Xiang, Zhang Xiaoming, Jia Ke. "RLS and its improved algorithms in MIMO-OFDM System", International Journal of Modelling, Identification and Control, Vol. 16, No. 3, pp. 246-250, Jul. 2012.
[8] Mukesh Patidar, Rupesh Dubey, Nitinkumar Jain and Saritakul Pariya, “Performance Analysis of WiMAX 802.16e Physical Layer Model”, International Conference on wireless communication, 2012 IEEE.
[9] Chin-Liang Wang and Shun-Sheng Wang and Hsiao-Ling Chang, “A Low-Complexity SLM Based PAPR Reduction Scheme for SFBC MIMO-OFDM Systems”, International Conference on Wireless Communication, 2011 IEEE.
[10] L. Yang, K. K. Soo, S. li, and Y. M. SU, “PAPR Reduction Using Low Complexity PTS to Construct of OFDM Signals Without Side Information”, IEEE Transactions on Broadcasting, Vol. 57, No. 2, pp. 4532-4539, June 2011.
[11] Chin-Liang Wang and Shun-Sheng Wang† and Hsiao-Ling Chang, “A Low-Complexity SLM Based PAPR Reduction Scheme for SFBC MIMO-OFDM Systems”, International Conference on Wireless Communication, pp. 345-352, 2011 IEEE.
[12] K. Y. Cho, B. S. Choi, Y. Takushima, and Y. C. Chung, B25.78-Gb/s operation of RSOA for next-generation optical access networks, IEEE Photon. Technol. Letter, vol. 23, no. 8, pp. 495–497, Apr. 2011.
[13] Divyang Rawal, Park Youn Ok and C. Vijaykumar, “A Novel training based QR-RLS channel estimator for MIMO OFDM systems”, Wireless Advanced (WiAD), 6th Conference on, IEEE 2010.
[14] Ke Chen and Xiaojing Huang, “A Novel Approach for Interference Suppression in Multi-Sub band Convolutional Coded OFDM System”, School of Electrical, Computer & Telecommunications University of Wollongong, Australia, 2010.
[15] Sen-Hung Wang, and Chih-Peng Li, “A Low-Complexity PAPR Reduction Scheme for SFBC MIMO-OFDM Systems”, IEEE Signal Processing Letters, Vol. 16, No. 11, November 2009.
[16] Yang Zhou and Tao Jiang, “A Novel Multi-Points Square Mapping Combined With PTS to Reduce PAPR of OFDM Signals Without Side Information”, IEEE Transactions on Broadcasting, Vol. 55, No. 4, December 2009.
[17] Wei Jiang and Daoben Li, “Convolutional Multi-code Multiplexing for OFDM Systems”, Beijing University of Posts and Telecommunications Beijing 100876, China, 2007.

Dolly Panthi, Munna Lal Jatav, "Survey Paper on Performance Evaluation of 4G and 5G System using Space Time Block Coding Technique," International Journal of Computer Sciences and Engineering, Vol.7, Issue.9, pp.181-185, 2019.

Agriculture is the main source of occupation which forms the backbone of our country. It involves the production of crops which may be either food crops or commercial crops. The productivity of crop yield is significantly influenced by various parameters such as rainfall, farm capacity, temperature, crop population density, humidity, irrigation, fertilizer application, solar radiation, type of soil, depth, tillage and soil organic matter. An accurate crop yield prediction support decision-makers in the agriculture sector to predict the yield effectively. Machine learning techniques and deep learning techniques play a significant role in the analysis of data for crop yield prediction. However, the selection of appropriate techniques from the pool of available techniques imposes challenges to the researchers concerning the chosen crop. In this paper, an analysis has been performed on various deep learning and machine learning techniques. To know the limitations of each technique, a comparative analysis is carried out in this paper. In addition to this, a suggestion is provided to further improve the performance of crop yield prediction.

Agriculture, crop yield prediction, productivity of crop yield, machine learning, deep learning

[1] B. Milovic, V. Radojevic, “Application of data mining in agriculture”, Bulgarian Journal of Agricultural Science, Vol.21, Issue.1, pp.26-34,2015.
[2] D. Ramesh, B. V. Vardhan, “Data mining techniques and applications to agricultural yield data”, International Journal of Advanced Research in Computer and Communication Engineering, Vol.2, Issue.9, pp.3477-80, 2013.
[3] S. Veenadhari, B. Mishra, C. D.Singh, “Soybean productivity modelling using decision tree algorithms” International Journal of Computer Applications, Vol.27, Issue.7, pp.11-15,2011.
[4] W. W. Guo, H. Xue, “Crop yield forecasting using artificial neural networks: A comparison between spatial and temporal models”, Mathematical Problems in Engineering, 2014.
[5] Shastry, H. A. Sanjay, M. Hegde, “A parameter based ANFIS model for crop yield prediction” IEEE International Advance Computing Conference (IACC), pp.253-257, 2015.
[6] Shah, A. Dubey, V. Hemnani, D. Gala, D. R. Kalbande, “Smart Farming System: Crop Yield Prediction Using Regression Techniques”. In Proceedings of International Conference on Wireless Communication, Springer, Singapore, pp. 49-56, 2018.
[7] F. N. Ogwueleka, “Crop growth prediction using self-organizing map and multilayer feed-forward neural network” American-Eurasian Journal of Sustainable Agriculture, Vol.5, Issue.2, pp.168-176, 2018.
[8] B.V. Vardhan, D. Ramesh, “Density based clustering technique on crop yield prediction”, International Journal of Electronics and Electrical Engineering, Vol.2, Issue.1, pp.56-59, 2014.
[9] P. Mohan, K. K. Patil, “Weather and Crop Prediction Using Modified Self Organizing Map for Mysore Region”, International Journal of Intelligent Engineering & Systems (IJIES), Vol.11, Issue.2, pp.192-199, 2017.
[10] A.Verma, A. Jatain, S.Bajaj, “Crop yield prediction of wheat using Fuzzy C Means clustering and neural network”, International Journal of Applied Engineering Research, Vol.13, Issue.11, pp.9816-9821, 2018.
[11] S. Suchithra, M. L. Pai, “Impact of Deep Neural Network on predicting application rate of fertilizers (Focus on coconut trees of Kerala northern coastal plain agro ecological unit)”. International Journal of Pure and Applied Mathematics, Vol.119, Issue.10, pp.451-466,2017.
[12] P. Nevavuori, N. Narra, T. Lipping, “Crop yield prediction with deep convolutional neural networks” Computers and Electronics in Agriculture, Vol.163, 2019.
[13] S. Khaki, L. Wang, “Crop yield prediction using deep neural networks”, Frontiers in plant science, Vol.10,2019.

M. Saranya, S. Sathappan, "Crop Yield Prediction Based on Data Mining Techniques: A Review," International Journal of Computer Sciences and Engineering, Vol.7, Issue.9, pp.186-188, 2019.

In this paper, various machine learning algorithms have been discussed. These algorithms are used for various purposes like data mining, image processing, predictive analytics, etc. to name a few. The main advantage of using machine learning is that, once an algorithm learns what to do with data, it can do its work automatically.

Machine Learning, Data Mining, Predictive Analytics, Image Processing, Algorithms

[1] T. M. Mitchell, Machine Learning, McGraw-Hill International, 1997.
[2] T.M. Mitchel, The Discipline of Machine Learning, CMU-ML-06-108, 2006
[3] N. Cristianini and J. Shawe-Taylor. An Introduction to Support Vector Machines. Cambridge University Press, 2000.
[4] E. Osuna, R. Freund, and F. Girosi. Support vector machines: training and applications. AI Memo 1602, MIT, May 1997.
[5] V. Vapnik. Statistical Learning Theory. John Wiley & Sons, 1998.
[6] C.J.C. Burges. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2):1-47, 1998.
[7] Taiwo Oladipupo Ayodele, Types of Machine Learning Algorithms, New Advances in Machine Learning, Yagang Zhang (Ed.), InTech, 2010
[8] T. Mitchell, W. Cohen, E. Hruschka, P. Talukdar, J. Betteridge, A. Carlson, B. Dalvi, M. Gardner, B. Kisiel, J. Krishnamurthy, N. Lao, K. Mazaitis, T. Mohamed, N. Nakashole, E. Platanios,A. Ritter, M. Samadi, B. Settles, R. Wang, D. Wijaya, A. Gupta, X. Chen, A. Saparov,M. Greaves, J. Welling, Never-Ending Learning, Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2014
[9] Pedregosa et al.,Scikit-learn: Machine Learning in Python, JMLR 12, pp. 2825-2830, 2011.
[10] Wang, J. and Jebara, T. and Chang, S.-F. Semi-supervised learning using greedy max- cut. Journal of Machine Learning Research , Volume 14(1), 771-800 2013
[11] Chapelle, O. and Sindhwani, V. and Keerthi, S. S. Optimization Techniques for Semi- Supervised Support Vector Machines, Journal of Machine Learning Research , Volume 9, 203–233, 2013
[12] J. Baxter. A model of inductive bias learning. Journal of Artificial Intelligence Research, 12:149–198, 2000.
[13] S. Ben-David and R. Schuller. Exploiting task relatedness for multiple task learning. In Conference on Learning Theory, 2003.
[14] W. Dai, G. Xue, Q. Yang, and Y. Yu, Transferring Naive Bayes classifiers for text classification .AAAI Conference on Artificial Intelligence, 2007.
[15] H. Hlynsson. Transfer learning using the minimum description length principle with a decision tree application. Master’s thesis, University of Amsterdam, 2007.
[16] Z. Marx, M. Rosenstein, L. Kaelbling, and T. Dietterich. Transfer learning with an ensemble of background tasks. In NIPS Workshop on Transfer Learning, 2005.
[17] R Conway and D Strip, Selective partial access to a database, In Proceedings of ACM Annual Conference, 85 - 89, 1976
[18] P D Stachour and B M Thuraisingham Design of LDV A multilevel secure relational database management system, IEEE Trans. Knowledge and Data Eng., Volume 2, Issue 2, 190 - 209, 1990
[19] R Oppliger, Internet security: Firewalls and beyond, Comm. ACM, Volume 40, Issue 5, 92 -102, 1997
[20] Rakesh Agrawal, Ramakrishnan Srikant, Privacy Preserving Data Mining, SIGMOD `00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data, Volume 29 Issue 2,Pages 439-450, 2000
[21] A. Carlson, J. Betteridge, B.Kisiel, B.Settles,E. R.Hruschka Jr,and T. M. Mitchell, Toward an architecture for never-ending language learning, AAAI, volume 5, 3, 2010
[22] X. Chen, A. Shrivastava, and A. Gupta, Neil: Extracting visual knowledge from web data, In Proceedings of ICCV, 2013.
[23] P. Donmezand J. G. Carbonell, Proactive learning: cost-sensitive active learning with multiple imperfect oracles. In Proceedings of the 17th ACM conference on In- formation and knowledge management, 619–628. ACM, 2008
[24] T. M.Mitchell, J. Allen, P. Chalasani, J. Cheng, O. Etzioni, M. N. Ringuetteand J. C. Schlimmer, Theo: A framework for self-improving systems, Arch. for Intelli- gence 323–356, 1991
[25] Gregory, P. A. and Gail, A. C. Self-supervised ARTMAP Neural Networks, Volume 23, 265-282, 2010
[26] Cour, T. and Sapp, B. and Taskar, B. Learning from partial labels, Journal of Machine Learning Research, Volume 12, 1501-1536 2012
[27] Adankon, M. and Cheriet, M. Genetic algorithm-based training for semi-supervised SVM, Neural Computing and Applications Volume 19(8), 1197-1206, 2010

K. Vinod Kumar, P. Santosh Kumar, "Review of Latest Advancements and Trends in Machine Learning," International Journal of Computer Sciences and Engineering, Vol.7, Issue.9, pp.189-192, 2019.

Compiler construction may be a wide used software engineering exercise, and therefore this paper presents a compiler system for accommodative computing. The final result of this paper is to produce a knowledge concerning compiler design and its implementation. In order to develop effective compilation techniques, it is important to understand the common characteristics of the programs during compilation. Although this paper concentrates on the implementation of a compiler, an overview that builds upon the compiler is additionally bestowed.

Lexer-Parser, Competence development, Compilation, Lexical analysis, Syntactic analysis reconfigurable hardware

[1] GerhardGoosInstitutProgrammstrukturenand Datenorganisation Fakultat furInformatik
[2]University at KarlsruheD-76128 KarlsruheGermanye mail: ggoos@ipd.info.uni-karlsruhe.de
[3] Niklaus WirthThis is a slightly revised version of the book published by Addison-Wesley in 1996ISBN 0- 201- 40353-6Zürich, November 2005.
[4]http://www.esa.informatik.tudarmstadt.de/twiki/pub/Staff/AndreasKochPublications/2001_ERSA01.pdf
[5]http://www.ijsrp.org/research-paper-0413/ijsrp-p16108.pdf

Khan Uzma Khatoon, "Compiler Basic Designing and Construction," International Journal of Computer Sciences and Engineering, Vol.7, Issue.9, pp.193-194, 2019.

According to the forecast that billions of devices will get connected to the Internet by 2020. All these devices will produce a huge amount of data that will have to be handled rapidly and in a feasible manner. It will become a challenge for real-time applications to handle this huge data while considering security issues as well as time constraints. The main highlights of cloud computing are on-demand service and scalability; therefore the data generated from IoT devices are generally handled in cloud infrastructure. Though, dealing with IoT application requests on the cloud exclusively is not a proficient result for some IoT applications particularly time-sensitive ones. These issues can be settled by utilizing another idea called, Fog computing. Fog computing has become one of the major fields of research from both academia and industry perspectives. The ongoing research commitments on few issues in fog computing are figuring out in this paper. At long last, this paper also highlights some open issues in fog with IoT, which will determine the future research direction for implementing Fog computing paradigm.

Fog computing, Internet-of-Things, Cloud computing, Cloud-based IoT

[1] L. Coetzee, J. Eksteen, The Internet of Things- Promise for the Future? An Introduction, IST Africa 2011, http://www.ISTafrica. org/Conference 2011
[2] L. Atzori et al., The Internet of Things: A survey, Comput. Netw.(2010), doi:10.1016/j.comnet.2010.05.010.
[3] Bolivar Torres et.al., Integration of an RFID reader to a wireless sensor network and its use to identify an individual carrying RFID tags, International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) Vol.1, No.4, December 2010
[4] B. B. P. Rao, P. Saluia, N. Sharma, A. Mittal and S. V. Sharma, "Cloud computing for the Internet of Things & sensing based applications," 2012Sixth International Conference on Sensing Technology (ICST), Kolkata, pp. 374-380, 2012.
[5] G. Joshi and S. Kim, “Survey, Nomenclature and Comparison of ReaderAnti-Collision Protocols in RFID,” IETE Tech. Rev.,Vol. 25, no. 5,
[6] R. Jason Weiss, Development Dimensions International. J. Philip Craiger, University of Nebraska–Omaha
[7] S. Maharajothi, Dr S. Adaekalavan: Deadline Based Resource Provisioning and Scheduling Algorithm for Scientific Workflows on Clouds, International Journal of Advance Research in Computer Science and Management Studies, Vol 4, Issue. 8, pp 60-66, August 2016, ISSN: 2321-7782 (Online)
[8] NIST, (2009). “Presentation on Effectively and Securely Using the Cloud Computing Paradigm v25,” http://csrc.nist.gov/groups/SNS/cloudcomputing/cloud-computing-v25.ppt, on August 29, 2009
[9] P. Mell and T. Grance: “The NIST definition of cloud computing (v15),” National Institute of Standards and Technology, Version 15, 10-7-09.
[10] Aazam, M., Huh, E.-N.: Fog computing and smart gateway based communication for a cloud of things. In: The Proceedings of IEEE Future Internet of Things and Cloud (FiCloud), Barcelona, Spain, pp. 27–29, 2014
[11] M.M. Islam, S.Morshed, and P.Goswami, "Cloud computing: A survey on its limitations and potential solutions," International Journal of Computer Science Issues, Vol.10, no.4, pp. 159--163, 2013.
[12] E.O. Yeboah-Boateng and K.A. Essandoh, "Factors influencing the adoption of cloud computing by small and medium enterprises in developing economies," International Journal of Emerging Science and Engineering, Vol.2, no.4, pp. 13--20, 2014.
[13] R.P. Padhy and M.R. Patra, "Managing IT operations in a cloud-driven enterprise: Case studies," American Journal of Cloud Computing, Vol.1, no.1, pp. 1--18, 2013.
[14] Atlam, H.F.; Alenezi, A.; Walters, R.J.; Wills, G.B.; Daniel, J. Developing an adaptive Risk-based access control model for the Internet of Things. In Proceedings of the 2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), Exeter, UK, 21–23, pp. 655–661, June 2017;
[15] Ai, Y.; Peng, M.; Zhang, K. Edge cloud computing technologies for the internet of things: A primer. Digit.Commun. Netw. 2017, in press.
[16] Bonomi, F.; Milito, R.; Zhu, J.; Addepalli, S. Fog computing and its role in the internet of things. In Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing-MCC ’12, Helsinki, Finland, 17 pp. 13–16, August 2012
[17] Manzalini and N. Crespi, “An Edge Operating System Enabling Anything-as-a-Service,” IEEE Commun. Mag., Vol. 54, no. 3, pp. 62–67, Mar. 2016.
[18] Wen, Z.; Yang, R.; Garraghan, P.; Lin, T.; Xu, J.; Rovatsos, M. Fog orchestration for internet of things services.IEEE Internet Comput., 21, 16–24, 2017
[19] Liu, Y.; Fieldsend, J.E.; Min, G. A Framework of Fog Computing: Architecture, Challenges and Optimization. IEEE Access, 4, 1–10, 2017.
[20] Peralta, G.; Iglesias-Urkia, M.; Barcelo, M.; Gomez, R.; Moran, A.; Bilbao, J. Fog computing based efficient IoT scheme for the Industry 4.0. In Proceedings of the2017 IEEE International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics, San Sebastian, Spain,; pp.1–6, 24–26 May 2017
[21] Luan, T.H.; Gao, L.; Li, Z.; Xiang, Y.; Wei, G.; Sun, L. Fog Computing: Focusing on Mobile Users at the Edge.arXiv2015, arXiv:1502.01815.
[22] Choi, N.; Kim, D.; Lee, S.; Yi, Y. Fog Operating System for User-Oriented IoT Services: Challenges and Research Directions. IEEE Commun. Mag. 2017, 55, 2–9.
[23] Mukherjee, M.; Matam, R.; Shu, L.; Maglaras, L.; Ferrag, M.A.; Choudhury, N. Security and Privacy in Fog Computing: Challenges. IEEE Access 2017, 5, 19293–19304.
[24] Yi, S.; Hao, Z.; Qin, Z.; Li, Q. Fog computing: Platform and applications. In Proceedings of the 3rdWorkshop on Hot Topics in Web Systems and Technologies, HotWeb 2015, Washington, DC, USA, pp. 73–78, 24–25 October 2016
[25] Giang, N.K.; Blackstock, M.; Lea, R.; Leung, V.C.M. Developing IoT applications in the Fog: A Distributed Dataflow approach. In Proceedings of the 5th International Conference Internet Things, IoT 2015, Seoul, Korea, pp. 155–162, 26–28 October 2015
[26] Ketel, M. Fog-Cloud Services for IoT. In Proceedings of the Southeast Conference, Kennesaw, GA, USA, pp. 262–264, 13–15 April 2017
[27] Alenezi, A.; Zulkipli, N.H.N.; Atlam, H.F.; Walters, R.J.; Wills, G.B.The Impact of Cloud Forensic Readiness on Security. In Proceedings of the 7th International Conference on Cloud Computing and Services Science (CLOSER 2017), Porto, Portugal, pp. 511–517, 24–26 April 2017
[28] Lee, K.; Kim, D.; Ha, D.; Rajput, U.; Oh, H. On security and privacy issues of fog computing supported the Internet of Things environment. In Proceedings of the 6th International Conference on the Network of the future (NOF), Montreal, QC, USA; 2015; pp. 1–3.
[29] Yin, B.; Shen, W.; Cheng, Y.; Cai, L.X.; Li, Q. Distributed resource sharing in fog-assisted big data streaming. In Proceedings of the 2017 IEEE International Conference on Communications, Chengdu, China, pp. 1–6, 27–30 October 2017
[30] Hung, C.L.; Yan, R.Y.; Wang, H.H. Parallel image dehazing algorithm based on GPU using fuzzy system and hybrid evolution algorithm. In Proceedings of the 2016 IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), Shanghai, China, pp. 581–583, 30 May–1 June 2016
[31] Hu, P.; Ning, H.; Qiu, T.; Song, H.; Wang, Y.; Yao, X. Security and Privacy Preservation Scheme of Face Identification and Resolution Framework Using Fog Computing in the Internet of Things. IEEE Internet Things J. 2017, 4, 1143–1155.
[32] Wang, L.; Liu, G.; Sun, L.A secure and privacy-preserving navigation scheme using spatial crowdsourcing in fog-based VANETs.Sensors 2017, 17, 668.
[33] Al Hamid, H.A.; Rahman, S.M.M.; Hossain, M.S.; Almogren, A.; Alamri, A.A Security Model for Preserving the Privacy of Medical Big Data in a Healthcare Cloud Using a Fog Computing Facility with Pairing-Based Cryptography. IEEE Access 2017, 5, 22313–22328.

Kalpit G. Soni, Hiren Bhatt, Dhaval Patel, "Fog Computing: A Look on Present Scenario and Hopes for Future Research," International Journal of Computer Sciences and Engineering, Vol.7, Issue.9, pp.195-200, 2019.

In this paper we evaluate the efficient, scalable and practical method for privacy-preserving using k-nearest neighbors (KNN) classification method for EMR data. The approach enables performing the widely used k-NN classification method in complex scenarios where none of the parties reveal their information while they can still cooperatively find the nearest matches. To development AES- S.BOX mapping in mix column privacy preserving model used for preserving the privacy of the patients data in a cloud assisted system as the complex information is needed to be maintained confidential and should not be revealed to public users other than the physicians. As we know AES is based on several mathematical perform for security purpose substitutions, permutation and transformation.

Cloud approach, privacy preserving, S.BOX mapping in mix column, KNN, MATLAB 2014a

[1] A. Andoni and P. Indyk. Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. In FOCS, 2006.
[2] M. Bellare, V. Tung Hoang, Sriram K., and P. Rogaway. Efficient garbling from a fixed-key block cipher. In S&P. IEEE, 2013.
[3] J. Boyar and R. Peralta “Concrete multiplicative complexity of symmetric functions in MFCS” Springer, 2006
[4] Brenner, perl, and Smith. hcrypt Secure Function Evaluation (SFE) project. https://hcrypt.com/sfe/.
[5] H. Carter, C. Lever, and P. Traynor. Whitewash: Outsourcing garbled circuit generation for mobile devices. In ACSAC. ACM, 2014.
[6] H. Carter, B. Mood, P. Traynor, and K. Butler. Secure outsourced garbled circuit evaluation for mobile phones. In USENIX Security. USENIX, 2013.
[7] D. Demmler, T. Schneider, and M. Zohner. Ad-hoc secure two-party computation on mobile devices using hardware tokens. In USENIX Security. USENIX, 2014.
[8] C. Gentry. “Fully homomorphic encryption using ideal lattices”. In STOC, 2009.
[9] A. Bessani, M. Correia, B. Quaresma, F. Andr´e, and P. Sousa, “Depsky: Dependable and secure storage in a cloud-of-clouds,” in Proceedings of the Sixth Conference on Computer Systems, 2011, pp. 31–46.
[10] E. Stefanov, M. van Dijk, A. Juels, and A. Oprea, “Iris: A scalable cloud file system with efficient integrity checks,” in Proceedings of the 28th Annual Computer Security Applications Conference, 2012, pp. 229–238.
[11] Y. Chen and R. Sion, “On securing untrusted clouds with cryptography,” in Proceedings of the 9th annual ACM workshop on Privacy in the electronic society. ACM, 2010, pp. 109– 114.
[12] R. Laurikainen, “Secure and anonymous communication in the cloud,” Aalto University School of Science and Technology, Department of Computer Science and Engineering, Tech. Rep. TKK-CSE-B10, 2010.
[13] Sushmita Ruj, Milos Stojmenovic and Amiya Nayak, “Privacy Preserving Access Control with Authentication for Securing Data in Clouds”in proceedings of the 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing
[14] Debajyoti Mukhopadhyay, Gitesh Sonawane, Parth Sarthi Gupta, Sagar Bhavsar, Vibha Mittal.” Enhanced Security for Cloud Storage using File Encryption” Department of Information Technology Maharashtra Institute of Technology
[15] Kawser Wazed Nafi1,2, Tonny Shekha Kar2, Sayed Anisul Hoque3, Dr. M. M. A Hashem4, “ A Newer User Authentication, File encryption and Distributed Server Based Cloud Computing security architecture”in proceeding of the (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 3, No. 10, 2011
[16] Du meng.” Data security in cloud computing” in yhe proceeding of the The 8th International Conference on Computer Science & Education (ICCSE 2013) April 26-28, 2013. Colombo, Sri Lanka

Anjali Kumari, Varsha Namdeo, "Privacy Preserving Using AES-Mapping in Mix Column Encryption Algorithm: Cloud Approach," International Journal of Computer Sciences and Engineering, Vol.7, Issue.9, pp.201-206, 2019.

In late 2008, Intel launched the core i7 processor. This processor was made as an intention to target business and markets for laptops and computers. This processor was made mainly for gaming, intensively graphics tasks etc. This processor can handle a wide variety of tasks at once and hence are best suited for those who wants a computer with powerful performance. For data crunching, this processor is the most suitable. It has a fast clock speed and integrate max 4 cores. It also has Virtualization Technology and Streaming SIMD Instructions. It also supports Intel Turbo Boost technology.

gaming;graphics;clockspeed;DDR3memory

[1] Gregory Austin, “INSTRUCTION-LEVEL PARALLELISM IN INTEL CORE I7 HASWELL” Dec 16,2016 https://www.eit.lth.se/fileadmin/eit/courses/edt621/Rapporter/2016/Gregory_Austin_EDT621_Report.pdf
[2] P. Sangeetha and M. Mythili, “FEATURES OF INTEL CORE i7 PROCESSORS” International Journal of Engineering Research and General Science Volume 3, Issue 2, March-April 2015 ISSN 2091-2730. http://pnrsolution.org/Datacenter/Vol3/Issue2/68.pdf
[3] Himanshi Grover and Devesh Agrawal, “DESIGN AND ARCHITECTURE OF INTEL’S CORE I7 PROCESSOR” Volume 2 Issue X, October 2014 ISSN: 2321-9653 https://www.ijraset.com/fileserve.php?FID=1034
[4] Tiwari, R. Sam and S. Shaikh, "Analysis and prediction of churn customers for telecommunication industry," 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Palladam, 2017, pp. 218-222. doi: 10.1109/I-SMAC.2017.8058343
[5] Manish Mishra, Piyush Shukla, Rajeev Pandey, "Assessment on different tools used for Simulation of routing for Low power and lossy Networks (RPL)", International Journal of Scientific Research in Network Security and Communication, Vol.7, Issue.4, pp.26-32, 2019
[6] M. Sora, J. Talukdhar, S. Majumder, P.H Talukdhar, U. Sharmah, "Word level detection of Galo and Adi language using acoustical cues", International Journal of Scientific Research in Computer Science and Engineering, Vol.1, Issue.1, pp.10-13, 2013
[7] A. Fasiku, Ayodeji Ireti, B. Olawale, Jimoh Babatunde, C. Abiola Oluwatoyin B., "Comparison of Intel Single-Core and Intel Dual-Core Processor Performance", International Journal of Scientific Research in Computer Science and Engineering, Vol.1, Issue.1, pp.1-9, 2013
[8] S. Shaikh, S. Rathi and P. Janrao, "Recommendation System in E-Commerce Websites: A Graph Based Approached," 2017 IEEE 7th International Advance Computing Conference (IACC), Hyderabad, 2017, pp. 931-934. doi: 10.1109/IACC.2017.0189
[9] S. Shaikh, S. Rathi and P. Janrao, "IRuSL: Image Recommendation Using Semantic Link," 2016 8th International Conference on Computational Intelligence and Communication Networks (CICN), Tehri, 2016, pp. 305-308. doi: 10.1109/CICN.2016.66
[10] S. Navadia, P. Yadav, J. Thomas and S. Shaikh, "Weather prediction: A novel approach for measuring and analyzing weather data," 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Palladam, 2017, pp. 414-417. doi: 10.1109/I-SMAC.2017.8058382

Erum Parkar, Naushin Khan, "Core i7: A Research into the Processor," International Journal of Computer Sciences and Engineering, Vol.7, Issue.9, pp.207-209, 2019.