Archive

Abstract:

For a neutral element [III] have introduced the concept of -distributive lattices which is a generalization of both -distributive and 1-distributive lattices. For a central element  of a nearlattice , we have discussed -distribitive nearlattices which is a generalization of both0-distributive semilattices and -distributive lattices. For an element  of nearlattice  a convex subnearlattice of  containing  is called an -ideal of . In this paper, we have given some properties of -distributivenearlattices. Finally, we have included a generalization of prime Separation Theorem in terms of annihilator -ideal.

Keywords:

Central element,0-distributive lattice,n-distributive lattice,n-annihilator,annihilator n-ideal,prime n-ideal,n-distributive nearlattice,

Refference:

A. S. A. Noor and M. A. Latif, Finitely generated n-ideals of a lattice, SEA Bull. Math., 22(1998), pp. 73-79
M. A. Latif and A. S. A. Noor, A generalization of Stone’s representation theorem, The Rajshahi University Studies(Part-B),31(2003), pp. 83-87.
M. AyubAli , A.S.A. Noor and Sompa Rani Poddar, n-distributive lattice, Journal of Physical Sciences, 16(2012), pp. 23-30.
P. Balasubramani and P.V. Venkatanarasimhan, Characterizations of the 0-distributive Lattices, Indian J. Pure Appl. Math., 32(3)(2001), pp. 315-324.
S. Akhter, A Study of Principal n-Ideals of a Nearlattice, Ph.D. Thesis, Rajshahi University, Bangladesh(2003).
S. Akhter and A. S. A. Noor, Semi Prime Filters in Join Semilattices, Annals of Pure and Applied Mathematics, 18(1)(2018), pp. 45-50. DOI: http://dx.doi.org/10.22457/apam.v18n1a6
S. Akhter and A. S. A. Noor, 1-distributive join semilattice, J. Mech. Cont. & Math. Sci., 7(2)(2013), pp. 1067-1076.
W. H. Cornish and A. S. A. Noor, Standard elements in a nearlattice, Bull. Austral. Math. Soc. 26(2)(1982), pp. 185-213.
Y. S. Powar and N. K. Thakare, 0-distributive semilattices, Journal of Pure and Applied Algebra, 56(1978), 469-475

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Abstract:

Predominantly in India, Agriculture is the most significant income generating segments and also a wellspring of endurance. Various occasional, financial and natural incidents impact the yield creation, yet erratic changes in these cases lead to an incredible misfortune for the Farmers. These dangers are to be decreased by utilizing reasonable mining methodologies on the identified data of soil type, temperature, environmental weights, mugginess and yield type. While, harvest and climate gauging can be anticipated by getting valuable bits of knowledge from this agricultural information that guides the Farmers to choose the yield, meanwhile they may need to plant for the expected year prompting extreme benefits. This paper presents an overview of different calculations utilized for climate, crop yield, and harvest forecast of the proposed crop yield prediction method using Least Squares Support Vector Machine (LS-SVM).

Keywords:

Crop yield prediction,Support Vector Machine,Least Squares Support Vector machine,Data Analytics,Agriculture,

Refference:

I. A.Na, W. Isaac, S. Varshney and E. Khan, “An IoT based system for remote monitoring of soil characteristics,” 2016 International Conference on Information Technology (InCITe) – The Next Generation IT Summit on the Theme – Internet of Things: Connect your Worlds, Noida, 2016, pp.316-320, doi: 10.1109/INCITE.2016.7857638.
II. AnupamaMahato, “Climate Change and its Impact on Agriculture”, International Journal of Scientific and Research Publications, Volume 4, Issue 4, April 2014.
III. Awanit Kumar, Shiv Kumar, “Prediction of production of crops using K-Means and Fuzzy Logic”, International Journal of Computer Science and Mobile Computing, Vol.4 Issue.8, August- 2015, pg. 44-56.
IV. Birthal, P.S., Kumar, S., Negi, D.S. and Roy, D. (2015), “The impacts of information on returns from farming: evidence from a nationally representative farm survey in India. Agricultural Economics”, 46: 549-561. doi:10.1111/agec.12181
V. Dhivya B, Manjula, Siva Bharathi, Madhumathi, “A Survey on Crop Yield Prediction based on Agricultural Data”, International Journal of Innovative Research in Science, Engineering and Technology, Vol. 6, Issue 3, March 2017.
VI. G. Ravichandran and R. S. Koteeshwari, “Agricultural crop predictor and advisor using ANN for smartphones,” 2016 International Conference on Emerging Trends in Engineering, Technology and Science (ICETETS), Pudukkottai, 2016, pp. 1-6. doi: 10.1109/ICETETS.2016.7603053.
VII. https://en.wikipedia.org/wiki/Linear_regression.
VIII. https://en.wikipedia.org/wiki/Nonlinear_regression.
IX. Iooss, Bertrand &Lemaître, Paul.(2014). A Review on Global Sensitivity Analysis Methods.Operations Research/ Computer Science Interfaces Series. 59. 10.1007/978-1-4899-7547-8-5.
X. JapneetKaur,” Impact of Climate Change on Agricultural Productivity and Food Security Resulting in Poverty in India”, Final Thesis, Master’s Degree Programme – Second Cycle, UniversitaCaFoscariVenezia, 2017.
XI. J. Shenoy and Y. Pingle, “IOT in agriculture”, 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, 2016, pp. 1456-1458.

XII. L. Leroux, C. Baron, B. Zoungrana, S. B. Traoré, D. Lo Seen and A. Bégué, “Crop Monitoring Using Vegetation and Thermal Indices for Yield Estimates: Case Study of a Rainfed Cereal in Semi-Arid West Africa,” in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 9, no. 1, pp. 347-362, Jan. 2016. doi: 10.1109/JSTARS.2015.2501343.
XIII. M. R. Bendre, R. C. Thool and V. R. Thool, “Big data in precision agriculture: Weather forecasting for future farming,” 2015 1st International Conference on Next Generation Computing Technologies (NGCT), Dehradun, 2015, pp. 744-750. doi: 10.1109/NGCT.2015.7375220.
XIV. M. Paul, S. K. Vishwakarma and A. Verma, “Analysis of Soil Behaviour and Prediction of Crop Yield Using Data Mining Approach,” 2015 International Conference on Computational Intelligence and Communication Networks (CICN), Jabalpur, 2015, pp. 766-771. doi: 10.1109/CICN.2015.156.
XV. N. Hemageetha, “A survey on application of data mining techniques to analyze the soil for agricultural purpose,” 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, 2016, pp. 3112-3117.
XVI. R. Mythili, MeenakshiKumari, ApoorvTripathi, Neha Pal, “IoT Based Smart Farm Monitoring System”, International Journal of Recent Technology and Engineering, ISSN: 2277-3878, Volume-8 Issue-4, November 2019.
XVII. S. Nagini, T. V. R. Kanth and B. V. Kiranmayee, “Agriculture yield prediction using predictive analytic techniques,” 2016 2nd International Conference on Contemporary Computing and Informatics (IC3I), Noida, 2016, pp. 783-788. doi: 10.1109/IC3I.2016.7918789.
XVIII. Soybeandataset, “https://archive.ics.uci.edu/ml/datasets.php?format=mat&task=cla&att=&area=life&numAtt=10to100&numIns=&type=mvar&sort=dateUp&view=list”.
XIX. Zhihua Zhang, Multivariate Time Series Analysis in Climate and Environmental Research, 2018, Springer Nature Switzerland.

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Abstract:

Based on the basic principles of thermodynamics, and heat transfer, this paper presented a model of a solar water heating system (SWHS) with the aim of improving on the performance of the system. The annual thermal performance of the SWHS was simulated on the TRNSYS platform. The typical Babylon weather situations, the fluctuations in water temperature within the storage tank, and the inlet and outlet temperature of the collector were investigated. Other parameters considered by the simulation include the sum of solar emission and the difference in heat collector efficiency. The development of a model simulating the SWHS is key to determining the parameters for operating the components. It makes room for selection of necessary parameters required in improving the overall performance of the SWHS. This study provides theoretical guidance for operating the solar hot water system.

Keywords:

Solar water heater,TRNSYS,Solar fraction,Storage,Efficiency,

Refference:

I. Abid, M., et al., An experimental study of solar thermal system with storage for domestic applications. Journal of Mechanical Engineering and Sciences, 2018. 12(4): p. 4098-4116.
II. Burch, J., et al., Simulation of an unglazed collector system for domestic hot water and space heating and cooling. Solar Energy, 2004. 77(4): p. 399-406.
III. El-Sebaii, A. and H. Al-Snani, Effect of selective coating on thermal performance of flat plate solar air heaters. Energy, 2010. 35(4): p. 1820-1828.
IV. Habeeb, L.J., D.G. Mutasher, and F.A.A. Ali, Cooling Photovoltaic Thermal Solar Panel by Using Heat Pipe at Baghdad Climate. International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS, 2017. 17(06).
V. Hassan, A. and M. Muhammadu, Design, Construction and Performance Evaluation of Solar Water Pump. IOSR Journal of Engineering, 2013. 2(4): p. 711-718.
VI. Klein, S., et al., A method of simulation of solar processes and its application. Solar Energy, 1975. 17(1): p. 29-37.
VII. Mourtada, R., et al. Parametric Analysis of Solar Water Heating Systems for Buildings in Lebanon. in 2018 4th International Conference on Renewable Energies for Developing Countries (REDEC). 2018. IEEE.
VIII. Shariah, A. and B. Shalabi, Optimal design for a thermosyphon solar water heater. Renewable Energy, 1997. 11(3): p. 351-361.
IX. Shariah, A., et al., Effect of thermal conductivity of absorber plate on the performance of a solar water heater. Applied Thermal Engineering, 1999. 19(7): p. 733-741.
X. Shrivastava, R., V. Kumar, and S. Untawale, Modeling and simulation of solar water heater: A TRNSYS perspective. Renewable and Sustainable Energy Reviews, 2017. 67: p. 126-143.
XI. Tsilingiris, P., Solar water-heating design—a new simplified dynamic approach. Solar Energy, 1996. 57(1): p. 19-28.
XII. Yusof, T., S. Anuar, and H. Ibrahim, A review of ground heat exchangers for cooling application in the Malaysian climate. Journal of Mechanical Engineering and Sciences, 2015: p. 1426-39.

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Abstract:

The key task within the healthcare field is usually the diagnosis of the disease. In case, a disease is actually diagnosed at earlier stage, then many lives might be rescued. Machine learning classification techniques can considerably help the healthcare field just by offering a precise and easy diagnosis of various diseases. Consequently, saving time both formed ical professionals and patients. As heart disease is usually the most recognized killer in the present day, it might be one of the most challenging diseases to diagnose. In this paper, we provide a survey of the various machine learning classification techniques that have been proposed to assist the healthcare professionals in diagnosing the cardiovascular disease. We started by giving the overview of various machine learning techniques along with describing brief definitions of the most commonly used classification techniques to diagnose heart disease. Then, we review representable research works on employing machine learning classification techniques in this field. Furthermore, a detailed comparison table of the surveyed papers is actually presented.

Keywords:

Heart Disease,Heart Disease Prediction,Machine Learning,Machine Learning Classification Techniques,

Refference:

I. Alsabti, K., Ranka, S., & Singh, V. (1997). An efficient k-means clustering algorithm.
II. Andrecut, M. (2009). Parallel GPU implementation of iterative PCA algorithms. Journal of Computational Biology, 16(11), 1593-1599.
III. Bowles, M. (2015). Machine learning in Python: essential techniques for predictive analysis. John Wiley & Sons.
IV. Caruana, R. (1997). Multitask learning. Machine learning, 28(1), 41-75.
V. Chaurasia, V., & Pal, S. (2014). Data mining approach to detect heart diseases. International Journal of Advanced Computer Science and Information Technology (IJACSIT) Vol, 2, 56-66.
VI. Chen, A. H., Huang, S. Y., Hong, P. S., Cheng, C. H., & Lin, E. J. (2011, September). HDPS: Heart disease prediction system. In 2011 Computing in Cardiology (pp. 557-560). IEEE.
VII. Harrington, P. (2012). Machine learning in action. Manning Publications Co..
VIII. Hiregoudar, S. B., Manjunath, K., &Patil, K. S. (2014). A survey: research summary on neural networks. International Journal of Research in Engineering and Technology, 3(15), 385-389.
IX. https://en.wikipedia.org/wiki/Boosting_(machine_learning)
X. https://en.wikipedia.org/wiki/Bootstrap_aggregating
XI. https://en.wikipedia.org/wiki/Instance-based_learning
XII. https://en.wikipedia.org/wiki/Principal_component_analysis
XIII. http://pypr.sourceforge.net/kmeans.html
XIV. https://webdocs.cs.ualberta.ca/~rgreiner/C-651/Homework2_Fall2008.html
XV. http://www.simplilearn.com/what-is-machine-learning-and-why-it- matters-article
XVI. Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996). Reinforcement learning: A survey. Journal of artificial intelligence research, 4, 237-285..
XVII. Keller, J. M., Gray, M. R., & Givens, J. A. (1985). A fuzzy k-nearest neighbor algorithm. IEEE transactions on systems, man, and cybernetics, (4), 580-585.
XVIII. Kotsiantis, S. B., Zaharakis, I., &Pintelas, P. (2007). Supervised machine learning: A review of classification techniques. Emerging artificial intelligence applications in computer engineering, 160, 3-24.
XIX. Lowd, D., &Domingos, P. (2005, August). Naive Bayes models for probability estimation. In Proceedings of the 22nd international conference on Machine learning (pp. 529-536).
XX. Liu, X., Wang, X., Su, Q., Zhang, M., Zhu, Y., Wang, Q., & Wang, Q. (2017). A hybrid classification system for heart disease diagnosis based on the RFRS method. Computational and mathematical methods in medicine, 2017.
XXI. Malav, A., Kadam, K., &Kamat, P. (2017). Prediction of heart disease using k-means and artificial neural network as Hybrid Approach to Improve Accuracy. International Journal of Engineering and Technology, 9(4), 3081-3085.
XXII. Meyer, D., & Wien, F. T. (2015). Support vector machines. The Interface to libsvm in package e1071, 28.
XXIII. Opitz, D., &Maclin, R. (1999). Popular ensemble methods: An empirical study. Journal of artificial intelligence research, 11, 169-198.[21] Zhou, Z. H. (2009). Ensemble Learning. Encyclopedia of biometrics, 1, 270-273.s
XXIV. Parthiban, G., &Srivatsa, S. K. (2012). Applying machine learning methods in diagnosing heart disease for diabetic patients. International Journal of Applied Information Systems (IJAIS), 3(7).
XXV. Richert, W. (2013). Building machine learning systems with Python. Packt Publishing Ltd.
XXVI. Rokach, L., &Maimon, O. (2005). Top-down induction of decision trees classifiers-a survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 35(4), 476-487.
XXVII. Salem, T. (2018). Study and analysis of prediction model for heart disease: an optimization approach using genetic algorithm. International Journal of Pure and Applied Mathematics, 119(16), 5323-5336.
XXVIII. Shalev-Shwartz, S., Singer, Y., &Srebro, N. Pegasos: Primal estimated subgradient solver for svm 2007b. URL http://ttic. uchicago. edu/shai/papers/ShalevSiSr07. pdf. A fast online algorithm for solving the linear svm in primal using sub-gradients.
XXIX. Sharma, V., Rai, S., &Dev, A. (2012). A comprehensive study of artificial neural networks. International Journal of Advanced research in computer science and software engineering, 2(10).
XXX. Sutton, R. S. (1992). Introduction: The challenge of reinforcement learning. In Reinforcement Learning (pp. 1-3). Springer, Boston, MA.
XXXI. Tan, K. C., Teoh, E. J., Yu, Q., &Goh, K. C. (2009). A hybrid evolutionary algorithm for attribute selection in data mining. Expert Systems with Applications, 36(4), 8616-8630.
XXXII. Vembandasamy, K., Sasipriya, R., &Deepa, E. (2015). Heart diseases detection using Naive Bayes algorithm. International Journal of Innovative Science, Engineering & Technology, 2(9), 441-444
XXXIII. Welling, M. (2011). A first encounter with Machine Learning. Irvine, CA.: University of California, 12.
XXXIV. Zhu, X., & Goldberg, A. B. (2009). Introduction to semi-supervised learning. Synthesis lectures on artificial intelligence and machine learning, 3(1), 1-130.
XXXV. Zhu, X. J. (2005). Semi-supervised learning literature survey. University of Wisconsin-Madison Department of Computer Sciences.

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Abstract:

The transformation of the Internet of Things (IoT) is changing numerous ideas, making them "Smartest". It has upset numerous territories of reality. Smart City is one of the key ideas of this revolution. In spite of the fact that urban areas are carefully and digitally changed, it still has hindrances en route. In this paper, we have dissected various productions for our Systematic Literature Review Protocol (SLRP). This studyhighlights the zones where the blockchain is utilized and decides the advantages of utilizing blockchain. The principle commitment is to investigate and recognize the hindrances and obstacles in Smart City Domain and how these obstacles are relieved by the blockchain innovation. This Systematic Study additionally addresses various difficulties and issues, for example, security, changelessness, interoperability, decentralization, protection, and trust in the advancement of Smart Cities. An overview of the precise research is likewise introduced that would help distinguish the most and least examined concerns tended to in this examination. This paper targets investigating how blockchain innovation is utilized in various SmartCity plans of action, what are the highlights of blockchainthat could believe it to be utilized past digital currencies. We trust that this investigation can motivate enthusiasm for hypothesis and exercise to substitute conversations here in this area to adhere to these confines.

Keywords:

Blockchain,Decentralization,immutability,Security,Smart City,Systematic Literature Review Protocol (SLRP),

Refference:

I. B. Kitchenham,”Procedures for performing systematic reviews.” Keele, UK, Keele University 33.2004 (2004): 1-26.
II. D. Efanov and P. Roschin, “The all-pervasiveness of the blockchain technology,” Procedia Comput. Sci., vol. 123, pp. 116–121, 2018.
III. D. Y. Liao and X. Wang, “Design of a Blockchain-Based Lottery System for Smart Cities Applications,” Proc. – 2017 IEEE 3rd Int. Conf. Collab. Internet Comput. CIC 2017, vol. 2017-Janua, pp. 272–282, 2017.
IV. F. E. P. Simona Ibba, Matteo Seu, Andrea Pinna, “City Sense: blockchain-oriented Smart Cities,” 2017.
V. F. R. Batubara, J. Ubacht, and M. Janssen, “Challenges of blockchain technology adoption for e-government: A systematic literature review,” ACM Int. Conf. Proceeding Ser., 2018.
VI. H. S. M. and R. G. António Brandão, “A Smart City’s Model Secured by Blockchain,” 2019.
VII. J. C. Ferreira, A. L. Martins, F. Gonçalves, and R. Maia, “A Blockchain and Gamification Approach for Smart Parking,” Lect. Notes Inst. Comput. Sci. Soc. Telecommun. Eng. LNICST, vol. 267, pp. 3–14, 2019.
VIII. J. H. Noh and H. Y. Kwon, “A Study on smart city security policy based on blockchain in 5G Age,” 2019 Int. Conf. Platf. Technol. Serv. PlatCon 2019 – Proc., pp. 1–4, 2019.
IX. J. H. P. Pradip Kumar Sharma, Seo Yeon Moon, “Block-VN A Distributed Blockchain Based Vehicular Network,” 2017.
X. J. Qiu, X. Liang, S. Shetty, and D. Bowden, “Towards Secure and Smart Healthcare in Smart Cities Using Blockchain,” 2018 IEEE Int. Smart Cities Conf. ISC2 2018, pp. 1–4, 2019.
XI. J. Rodrigues and A. Cardoso, “Blockchain in Smart Cities: An Inclusive Tool for Persons with Disabilities,” 2019 Smart Cities Symp. Prague, SCSP 2019 – Proc., no. 2018, pp. 1–6, 2019.
XII. J. Sun, J. Yan, and K. Z. K. Zhang, “Blockchain-based sharing services: What blockchain technology can contribute to smart cities,” Financ. Innov., vol. 2, no. 1, 2016.
XIII. K. Al Harthy, F. Al Shuhaimi, and K. K. Juma Al Ismaily, “The upcoming Blockchain adoption in Higher-education: Requirements and process,” 2019 4th MEC Int. Conf. Big Data Smart City, ICBDSC 2019, pp. 1–5, 2019.
XIV. K. Biswas and V. Muthukkumarasamy, “Securing smart cities using blockchain technology,” Proc. – 18th IEEE Int. Conf. High Perform. Comput. Commun. 14th IEEE Int. Conf. Smart City 2nd IEEE Int. Conf. Data Sci. Syst. HPCC/SmartCity/DSS 2016, pp. 1392–1393, 2017.
XV. K. Kotobi and M. Sartipi, “Efficient and Secure Communications in Smart Cities using Edge, Caching, and Blockchain,” 2018 IEEE Int. Smart Cities Conf. ISC2 2018, pp. 1–6, 2019.
XVI. L. Cui, G. Xie, Y. Qu, L. Gao, and Y. Yang, “Security and privacy in smart cities: Challenges and opportunities,” IEEE Access, vol. 6, pp. 46134–46145, 2018.
XVII. M. Altulyan, L. Yao, S. S. Kanhere, X. Wang, and C. Huang, “A unified framework for data integrity protection in people-centric smart cities,” Multimed. Tools Appl., 2019.
XVIII. P. K. Sharma and J. H. Park, “Blockchain based hybrid network architecture for the smart city,” Futur. Gener. Comput. Syst., vol. 86, pp. 650–655, 2018.
XIX. R. A. Salha, M. A. El-Hallaq, and A. I. Alastal, “Blockchain in Smart Cities: Exploring Possibilities in Terms of Opportunities and Challenges,” J. Data Anal. Inf. Process., vol. 07, no. 03, pp. 118–139, 2019.
XX. R. G. António Brandão, Henrique São Mamede, “Systematic Review of the Literature, Research on Blockchain Technology as Support to the Trust Model Proposed Applied to Smart Places,” 2018.
XXI. R. Paul, P. Baidya, S. Sau, K. Maity, S. Maity, and S. B. Mandal, “IoT Based Secure Smart City Architecture Using Blockchain,” Proc. – 2nd Int. Conf. Data Sci. Bus. Anal. ICDSBA 2018, pp. 215–220, 2018.
XXII. R. Rivera, J. G. Robledo, V. M. Larios, and J. M. Avalos, “How digital identity on blockchain can contribute in a smart city environment,” 2017 Int. Smart Cities Conf. ISC2 2017, vol. 00, no. c, 2017.
XXIII. R. Zhang et al., “Exploring the Attack Surfaces in Blockchain Enabled Smart Cities,” 2018 IEEE Int. Smart Cities Conf. ISC2 2018, vol. 890, no. 1, pp. 1–4, 2019.
XXIV. R. Zhang, R. Xue, and L. Liu, “Security and privacy on blockchain,” ACM Comput. Surv., vol. 52, no. 3, 2019.
XXV. S. Aggarwal, R. Chaudhary, G. S. Aujla, N. Kumar, K. K. R. Choo, and A. Y. Zomaya, “Blockchain for smart communities: Applications, challenges, and opportunities,” J. Netw. Comput. Appl., vol. 144, no. July, pp. 13–48, 2019.
XXVI. S. Li, “Application of blockchain technology in smart city infrastructure,” Proc. – 2018 IEEE Int. Conf. Smart Internet Things, SmartIoT 2018, pp. 276–282, 2018.
XXVII. T. S. F. Orecchini, A. Santiangeli, F. Zuccari, Alessandra Pieroni(&), “Blockchain Technology in Smart City: A New Opportunity for Smart Environment and Smart Mobility,” 2019.
XXVIII. V. N. P. and C. N. Patel, “Blockchain Technology: An Aid to the Governance of Smart Cities,” 2019.
XXIX. V. Skwarek, “Blockchains as security-enabler for industrial IoT-applications,” Asia Pacific J. Innov. Entrep., vol. 11, no. 3, pp. 301–311, 2017.
XXX. W. S. and C. X. Yi Zhang, “Blockchain in Smart City Development—The Knowledge Governance Framework in Dynamic Alliance,” 2019.
XXXI. Y. C. Deeraj Nagothu, Ronghua Xu, Seyed Yahya Nikouei, “A Microservice-enabled Architecture for Smart Surveillance using Blockchain Technology,” 2018.
XXXII. Y. Qian, Z. Liu, J. Yang, and Q. Wang, “A Method of Exchanging Data in Smart City by Blockchain,” Proc. – 20th Int. Conf. High Perform. Comput. Commun. 16th Int. Conf. Smart City 4th Int. Conf. Data Sci. Syst. HPCC/SmartCity/DSS 2018, no. 51578128, pp. 1344–1349, 2019.
XXXIII. Ž. Turk and R. Klinc, “Potentials of Blockchain Technology for Construction Management,” Procedia Eng., vol. 196, no. June, pp. 638–645, 2017.

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Abstract:

Telescopic Self-Centering braces are one of the very successful examples of Self-Centering braces which perform well in seismic loading. In this study, a new example of Telescopic Self-Centering brace is introduced, which has superior features over other telescopic braces. These include: high axial load capacity, use of shorter cables in brace construction, simplicity of construction, use of separate cables for compressive and traction modes, less fatigue in cyclic loads and, allowing for more dynamic loading cycles. In this paper, a sample was designed with an axial force capacity of 300kN.Modeling of behavior (DT-SCED) was accurately expressed using numerical relationships. Nonlinear incremental stiffness analysis method was also used to calculate the hysteresis brace behavior. The cyclic load test was applied to this brace and the result showed complete Self-Centering behavior. The results are compared with numerical relationships that were in good agreement.

Keywords:

Telescoping Self-Centering Energy-Dissipative Brace (DT-SCED),Cyclic Load Test,Nonlinear Incremental Stiffness Analysis,

Refference:

I. 10. Alam, S., Rafiqul Haque, A.“Cyclic Performance of a Piston Based Self-Centering Bracing System,” Structures Congress, 2015, 1:2360-2372.

II. 4 Christopoulos, C., Filiatrault, A., & Folz, B. (2002a). Seismic response of self-centring hysteretic SDOF systems. Earthquake Engineering & Structural Dynamics. 31(5), 1131-1150.

III. 5Christopoulos, C., Filiatrault, A., Uang, C-M., & Folz, B. (2002b). Posttensioned Energy Dissipating Connections for Moment-Resisting Steel Frames. J. Struct. Eng. 128(9), 1111-1120.

IV. 6 Christopoulos, C., Tremblay, R., Kim, H.-J., & Lacerte, M. (2008). Self-Centering Energy Dissipative Bracing System for the Seismic Resistance of Structure: Development and Validation. Journal of Structural Engineering, 134(1), 96-107.

V. 11 Erochko, j.Christopoulos, C., Tremblay, R. (2014) “Design, Testing, and Detailed Component Modeling of a High-Capacity Self-Centering Energy-Dissipative Brace,” ASCE Journal of Structural Engineering, 131:370-381.

VI. 9 Erochko, J., Christopoulos, C., & Tremblay, R. (2011). Design and Testing of an Enhanced-Elongation Telescoping Self-Centering Energy-Dissipative Brace. J. Struct. Eng. 137 (5), 589-599.

VII. 3 Filiatrault, A., Tremblay, R., & Kar, R. (2000). Performance Evaluation of Friction Spring Seismic Damper. J. Struct. Eng. 126 (4), 491-499.

VIII. 1 Nims, D.K., Richter, P.J., & Bachman, R.E. (1993). The Use of the Energy Dissipating Restraint for Seismic Hazard Mitigation. Earthquake Spectra, 9(3), 467-489.

IX. 7 Rojas, P., Ricles, J.M., & Sause, R. (2005). Seismic Performance of Post-tensioned Steel Moment Resisting Frames With Friction Devices. J. Struct. Eng. 131(4), 529-540.

X. 2 Tsopelas, T., Constantinou, M.C. (1994). Experimental and Analytical Study of a System Consisting of Sliding Bearings and Fluid Restoring Force/Damping Devices (NCEER-94-0014). Buffalo, NY: Dept. ofCiv. Eng., State University of New York at Buffalo.

XI. 8 Zhu, S., & Zhang, Y. (2008). Seismic Analysis of Concentrically Braced Frame Systems with SelfCentering Friction Damping Braces.J.Struct.Eng.134(1),121-131.

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Abstract:

Perching on cot, perching on recliner, obtaining out of cot and step dancing (ambulating standing, walking round the room) somewhere is troublesome for the older folks. Ambulating with facilitate of the folks or oversight is known jointly of the key causes of patient falls in hospitals and rest home thus we tend to use Artificial Intelligent and Machine Learning for top falls risks of older folks. Machine learning is associate algorithmic rule that's used for predicting outcomes accurately. we tend to incontestable 2 datasets that embrace time in seconds, frontal axis of acceleration, vertical axis of acceleration, and Lateral axis of acceleration, label of activity, frequency, phase,  received signal strength indicator and Id of antenna reading sensing element. such a big amount of technological solutions area unit foreseen for bed existing detection employing a style of sensors that area unit fastened with older folk body, their cot or around  somewhere with context to the older folks orfloor.

Keywords:

Artificial Intelligent,Machine Learning,RFID(RadioFrequency Identification) sensor,Decision Tree,SVM tree,Data analytics,K-Means,Naïve Bayes theorem,

Refference:

I. A. Godfrey, A. Bourke, G. O´laighin, P. van de Ven, and J. Nelson. Activity classification using a single chest mounted tri-axial accelerometer. Med. Eng. Phys.,33(9):1127–1135, 2011.
II. A. M. Khan, A. Tufail, A. M. Khattak, and T. H. Laine. Activity recognition on smartphones viasensor-fusion and KDA-based SVMs. Int. J. Distrib. Sens.Netw., 2014:e503291, 2014.
III. B. Najafi, K. Aminian, A. Paraschiv-Ionescu, F. Loew,C. Bula, and P. Robert. Ambulatory system for human motion analysis using a kinematic sensor: Monitoring of daily physical activity in the elderly. IEEE Trans. Biomed. Eng., 50(6):711–723, 2003.
IV. D. C. Ranasinghe, R. L. Shinmoto Torres, A. P. Sample, J. R. Smith, K. Hill, and R. Visvanathan. Towards falls prevention: A wearable wireless and battery-less sensing and automatic identification tag for real time monitoring of human movements. In Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

V. D. C. Ranasinghe, R. L. Shinmoto Torres, K. Hill, and R. Visvanathan. Low cost and batteryless sensor-enabled radio frequency identification tag based approaches to identify patient bed entry and exit posture transitions. Gait & Posture, 39(1):118–123, 2014.
VI. D. Karantonis, M. Narayanan, M. Mathie, N Lovell, and B. Celler. Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring. IEEE Trans. Inf. Technol. Biomed.,10(1):156–167, 2006.
VII. F. Miskelly. A novel system of electronic tagging in patients with dementia and wandering. Age and Ageing,33(3):304–306, 2004.
VIII. H. He and E. Garcia. Learning from imbalanced data.IEEE Trans. Knowl. Data Eng., 21(9):1263–1284,2009.
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XII. L. Gao, A. K. Bourke, and J. Nelson. Sensor positioning for activity recognition using multiple accelerometer-based sensors. In European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), 2013.
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XXII. Shinmoto Torres, R. L., Visvanathan, R., Hoskins, S., van den Hengel, A., Ranasinghe, D. C. (2016). Effectiveness of a batteryless and wireless wearable sensor system for identifying bed and chair exits in healthy older people. Sensors, 16(4), 546.
XXIII. T. Kaufmann, D. C. Ranasinghe, M. Zhou, and C. Fumeaux. Wearable quarter-wave folded microstrip antenna for passive UHF RFID applications. Int. J. Antennas Propag., 2013.
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XXV. Wickramasinghe, A., Ranasinghe, D. C. (2015, August). Recognising Activities in Real Time Using Body Worn Passive Sensors With Sparse Data Streams: To Interpolate or Not To Interpolate?. In proceedings of the 12th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (pp.21-30). ICST.
XXVI. Wickramasinghe, A., Ranasinghe, D. C., Fumeaux, C., Hill, K. D., Visvanathan, R. (2016), ‘Sequence Learning with Passive RFID Sensors for Real Time Bed-egress Recognition in Older People,’ in IEEE Journal of Biomedical and Health Informatics

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Abstract:

A numerical simulation is proposed for the thermal performance enhancement of Cascade Thermal Energy Storage System (CTESS)of paraffin wax Phase Change Materials (PCMs), by using Metal Foam (MF). Both melting and solidification processes were investigated. Copper foam with different porosities was used as MF and air as Heat Transfer Fluid (HTF).The numerical study includes charging and discharging processes at different velocities of (HTF) for three systems: CTESS with MF in the PCM side(MF-CTES),CTESS with MF in the fluid side(MF-AIR) and CTESS with MF in both PCM and fluid sides(MF-ALL).A numerical simulation by using CFD ANSYS FLUENT software package (Version 19) was done for the problem. The main results showed that by using metal foam in both sides (MF-ALL), the heat transfer enhanced greatly; it was between (53% -84%) in charging process and between (60% -86%) in discharging process, compared to the improvement obtained by previous work (Hiba and Ihsan [VI-IX])which ranged between (20.96 % to 42.04%) and (25.31% to 54.92%) for charging and discharging process respectively. This enhancement increases with increasing velocity and also the time of melting and solidification process reduced compared with (MF-CTES) and (MF- AIR).

Keywords:

Cascade Thermal Energy Storage,Metal Foam,Charging and Discharging Process,Numerical Simulation,

Refference:

I. Atul Sharma, V.V. Tyagi, C.R. Chen and D. Buddhi, “Review on thermal energy storage with phase change materials and applications”, Department of Mechanical Engineering, Kun Shan University, Renewable and Sustainable Energy Reviews 13 (2009) 318–345.

II. Ban M. Hasan,” Experimental and Numerical Model of a Phase Change Material (PCM) with Thermal Conductivity Enhancers”, M.Sc. Thesis, Al-Mustansiriyah University, (2015).

III. Bernardo Buonomo, DavideErcole, OronzioManca and Sergio Nardini, “Thermal Behaviors of Latent Thermal Energy Storage System with PCM and Aluminum Foam”, international journal of heat and technology, Vol. 34, Special Issue 2, October 2016, pp. S359-S364.

IV. ChenJianqing, Donghuiyanga, Jinghua Jiang, AibinMaa and Dan Song,” Research progress of phase change materials (PCMs) embedded with metal foam”, College of Mechanics and, Hohai University Procedia Materials Science 4 (2014) 389 – 394.

V. D. Zhou and C.Y. Zhao, “Experimental investigations on heat transfer in phase change materials (PCMs) embedded in porous materials”, School of Engineering, University of Warwick, Applied Thermal Engineering 31 (2011) 970 – 977.

VI. Hiba A. Hasan and Ihsan Y. Hussain, “Experimental Investigation of Thermal Performance Enhancement of Cascade Thermal Energy Storage System by Using Metal Foam”, IJMET, Volume 9, Issue 9, September 2018, pp. 1537-1549.

VII. Hiba A. Hasan and Ihsan Y. Hussain, “Simulation and Testing of Thermal Performance Enhancement for Cascade Thermal Energy Storage System by Using Metal Foam”, IJMME-IJENS Vol: 18 No. 05.

VIII. Hiba A. Hasan and Ihsan Y. Hussain, “Theoretical Formulation and Numerical Simulation of Thermal Performance Enhancements for Cascade Thermal Energy Storage Systems”, International Conference on Engineering Sciences, IOP Conf. Series: Materials Science and Engineering 433 (2018) 0112043.

IX. Hiba A. Hasan and Ihsan Y. Hussain, “Thermal Performance Enhancement of Phase Change Materials (PCMs) by Using Cascade Thermal Energy Storage (CTES) System with Metal Foam”, Ph.D. Dissertation, University of Baghdad – College of Engineering, 2018.

X. IoanSarbu and AlexandruDorca,” Review on heat transfer analysis in thermal energy storage using latent heat storage systems and phase change materials”, Department of Building Services Engineering, Polytechnic University of Timisoara, (2018).

XI. Marwah A. Jasim and Ihsan Y. Hussain, “Thermal Performance Enhancement of Phase Change Materials (PCMs) by Using Metal Foams”, Al-Nahrain Journal for Engineering Sciences (NJES) Vol.20 No.1, 2017 pp.235 – 249.

XII. Xue Chen, Xiaolei Li, Xinlin Xia 2, Chuang Sun and Rongqiang Liu, “Thermal Performance of a PCM-Based Thermal Energy Storage with Metal Foam Enhancement”, School of Mechatronics Engineering, Harbin Institute of Technology, Key Laboratory of Aerospace Thermophysics of MIIT, Harbin Institute of Technology, MPDI Journal.

XIII. Y. Tian and C.Y. Zhao, “Thermal and exergetic analysis of Metal Foam-enhanced Cascaded Thermal Energy Storage (MF-CTES)”, School of Engineering, University of Warwick, International Journal of Heat and Mass Transfer 58 (2013) 86–96.

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Abstract:

The day to day life billions of data are transferred across the internet using millions of devices. These Transferred data are theft or falsifying the original content by the intermediates when transferred from source to destination. So that data is transferred in a secure manner it does not theft or replicate. In the Virtual active key-based encryption a new idea of one-time active dynamic key is used (i.e.) while information transferred between the source and destination the original data is encoded with this key and the algorithm. A Secure un disclosed or secret key is created and applied to single packet and the different one-time active key is applied for consecutive packets, which is a protected communication context, the information to be sent is encoded with the Advanced Encryption Standard (AES) algorithm. The in-between nodes authenticate the acknowledged data packets if the received packet is untruthful or malicious data transmitted by any intruder such packets are noticed and removedor else the data is transferred to the succeeding node. In this Encryption method, there are two modes of operations that are carried out they are VABEK I and VABEK II [VABEK-Virtual Active Based Encryption Keying]. In VABEK I every node observe their adjacent node and in VABEK II every node arbitrarily chooses the nodes and track them. Thus, the two methods check all units and check those data packets if the data is a malicious one its dropped.

Keywords:

WSN,One-time dynamic active key,VABEK-I,VABEK-II,AES,

Refference:

I. Abd-alghafar, I ,Abdullah, B.,., Salama, G. I., Abd-alhafez, A. Performance evaluation of a genetic algorithm-based approach to network intrusion detection system Proceedings of the International Conference on Aerospace Sciences and Aviation Technology 2009 Cairo.

II. Breveglieri, L, Macchetti, M., Atasu, K. Efficient AES implementations for ARM based platforms. In: Symposium on Applied Computing, pp. 841–845. ACM, New York (2004)

III. Chen, H.-H ,Liu, H ,Khanna, R.,.,. Reduced complexity intrusion detection in sensor networks using genetic algorithm Proceedings of the IEEE International Conference on Communications June 2009 152-s2.0-7044948512210.1109/ICC.2009.5199399

IV. Eboka, A. O, Aghware, F. O,Ojugo, A. A.,.,Okonta, O. E., Yoro, R. E.,.Genetic algorithm rule-based intrusion detection system (GAIDS)Journal of Emerging Trends in Computing and Information Sciences 20123811821194.

V. F. Zao ,J. Liu, Y. Zhang, and, “Robust distributed node localization with error management,” in Proceeding of the 7th ACM International Symposium on Mobile Ad-Hoc Networking and Computing 2006.pp. 250–261, Florence, Italy, May 2006.

VI. J. Leonard, D. Moore, , D. Rus, and S. Teller, “Robust distributed network localization with noisy range measurements, “in Proceedings of the 2nd International Conference on Embedded Networked Sensor Systems 2004, pp. 50–61, November 2004.

VII. K. Gaj and P. Chodowiec, Very compact FPGA implementation of the AES algorithm. 5th Int. Workshop on Cryptographic Hardware and Embedded Systems (CHES 2003), pages 319-333,Germany, Sept. 8-10, 2003.

VIII. Lade, S. Dhak, B. S. An evolutionary approach to intrusion detection system using genetic algorithm International Journal of Emerging Technology and Advanced Engineering201222632637.

IX. M. Benaissa. And T. Good AES on FPGA from the fastest to the smallest.7th Int. Workshop on Cryptographic Hardware and Embedded Systems (CHES 2005), pages 427-440, Edinburgh, UK, Aug.09-01-2005.

X. M. Rabaey,, C. Savarese, J. and K. Langendoen, “Robust positioning algorithms for distributed ad-hoc wireless sensor networks,” in Proceedings of the 2002 USENIX Annual Technical Conference on General Track, pp. 317–327, USENIX Association, Berkeley, USA, 2002.

XI. N.Reijers,.K.Langendoen “Distributed localization in wireless sensor networks: a quantitative comparison,” Computer Networks, vol. 43, no. 4, pp. 499–518, 2003.

XII. Schwabe, P,Bernstein, D.J. New AES software speed records. In: Chowdhury, D.R., Rijmen, V., Das, A. INDOCRYPT 2008. LNCS, vol. 5365, pp. 322–336. Springer, Heidelberg (2008)

XIII. Schwabe, P,Käsper, E.,.: Faster and timing-attack resistant AES-GCM. In: Clavier, C., Gaj, K. CHES 2009. LNCS, vol. 5747, pp. 1–17. Springer, Heidelberg (2009)

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Abstract:

The advancement of large scale online social networks, online data sharing is turning out to be pervasive consistently. Both positive and negative information is spreading through online social networks. It centres on the negative data issues, for example, online rumours. Blocking of online rumour is one of the major issues in large scale social media networks. Hostile rumours can lead to confusion in the public eye and consequently should be quickly as fast as time permits in the wake of being distinguished. For this we used hybrid SVM, Naive Bayes and KNN algorithm. We will probably limit the impact of the rumour which is the quantity of clients that have acknowledged and sent the rumour by obstructing a specific subset of hubs

Keywords:

Rumour,malicious,Hybrid SVM,Naive Bayes,KNN,

Refference:

I. Castillo, C., Mendoza, M., &Poblete, B. (2011), March. Information credibility on twitter. In Proceedings of the 20th international conference on World wide web (pp. 675-684).

II. Kwon S Cha, M. Jung, K., Chen, W. & Wang, Y. (2013), November. Aspects of rumor spreading on a microblog network. In: International Conference on Social Informatics. Springer, Cham.

III. Kwon S Cha, M., Jung, K., Chen, W., & Wang, Y. (2013), December. Prominent features of rumor propagation in online social media. In: 2013 IEEE 13th International Conference on Data IEEE.

IV. Mendoza, M., Poblete, B., & Castillo, C. (2010), July. Twitter under crisis: Can we trust what we RT?. In: Proceedings of the first workshop on social media analytics (pp. 71-79).

V. Qazvinian, V., Rosengren, E., Radev, D. R., & Mei, Q. (2011), July. Rumor has it: Identifying misinformation in microblogs. In Proceedings of the conference on empirical methods in natural language processing (NPL). Association for Computational Linguistics.

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Abstract:

Picture dealing with is a strategy for changing over an image into cutting edge structure by playing out certain technique on it, in order to get the trademark features of that image. Face recognition is one of numerous utilizations of computerized picture preparing. Monitors placed too close or too far away may cause problems that may lead to eyestrain. Design is to implement automatic alert based on distance. Web camera can be used for capturing human head positions and separate the background from foreground head positions. Then face can be detected and recognized using image processing. Finally, the distance from monitor to face via web camera is calculated. If the distance is minimum to pre-define threshold value means, alert will be automatically generated and intimated to users without using any sensors.

Keywords:

Image processing,Face Recognition,

Refference:

I. C. Guo and L. Zhang, “A novel multi-resolution spatiotemporal saliency detection model and its applications in image and video compression”, TIP, vol. 19, no. 1, pp. 185–198, 2010
II. F. Perazzi, P. Kr¨ahenb¨uhl, Y. Pritch, and A. Hornung, “Saliency filters: Contrast based filtering for salient region detection”, in CVPR. IEEE, 2012, pp. 733–740
III. L. Itti, C. Koch, and E. Niebur, “A model of saliency-based visual attention for rapid scene analysis”, TPAMI, no. 11, pp. 1254–1259, 1998
IV. M. Cheng, N. J. Mitra, X. Huang, P. H. Torr, and S. Hu, “Global contrast based salient region detection”, TPAMI, vol. 37, no. 3, pp. 569–582, 2015
V. M. Donoser, M. Urschler, M. Hirzer, and H. Bischof, “Saliency driven total variation segmentation”, ICCV, IEEE, 2009, pp. 817–824
VI. M.-M. Cheng, J. Warrell, W.-Y. Lin, S. Zheng, V. Vineet, and N. Crook, “Efficient salient region detection with soft image abstraction”, ICCV, 2013, pp. 1529–1536
VII. P. Jiang, H. Ling, J. Yu, and J. Peng, “Salient region detection by ufo: Uniqueness, focusness and objectness”, in ICCV, 2013, pp. 1976–1983
VIII. Q. Yan, L. Xu, J. Shi, and J. Jia, “Hierarchical saliency detection”, in CVPR. IEEE, 2013, pp. 1155–1162
IX. S. Frintrop, G. M. Garcia, and A. B. Cremers, “A cognitive approach for object discovery”, ICPR, IEEE, 2014, pp. 2329–2334
X. S. Frintrop, T. Werner, and G. M. Garc´ıa, “Traditional saliency reloaded: A good old model in new shape”, in CVPR, 2015, pp. 82–90

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Abstract:

In recent years, there is a striking surge in the availability of porn images and other such sensitive content on the Internet.  Filtering of image porn has become one of the big challenges for searches; they are tied to finding methods to filter porn images and videos. Social media network is interested in filtering porn images from normal ones. The main objective of the proposed “Intelligent System to Prevent the Spreading of Sensitive Content Online” is to reduce the risk of harassment to a large extent by preventing anti-social elements from uploading such obscene content online. For attaining the ultimate goal, we will be using CNN algorithm to detect pornographic content. By RGB Channel Shifting, pixels of those pornographic contents will be corrupted in the device of the person trying to upload it on social media or internet. By using this “Intelligent System to Prevent the Spreading of Sensitive Content Online” we can prevent spreading of pornographic images/videos and thus avoid the harmful effects caused by these obscene practices.

Keywords:

CNN algorithm,RGB channel shifting,pornographic content,

Refference:

I. B. Liu, J. Su, Z. Lu and Z. Li, “Pornographic Images Detection Based on CBIR and Skin Analysis,” 2008 Fourth International Conference on Semantics, Knowledge and Grid, Beijing, 2008, pp. 487-488. doi: 10.1109/SKG.2008.48

II. H. Zhu, S. Zhou, J. Wang and Z. Yin, “An algorithm of pornographic image detection,” Fourth International Conference on Image and Graphics (ICIG 2007), Sichuan, 2007, pp. 801-804. doi: 10.1109/ICIG.2007.29

III. I. M. A. Agastya, A. Setyanto, Kusrini and D. O. D. Handayani, “Convolutional Neural Network for Pornographic Images Classification,” 2018 Fourth International Conference on Advances in Computing, Communication & Automation (ICACCA), Subang Jaya, Malaysia, 2018, pp. 1-5. doi: 10.1109/ICACCAF.2018.8776843

IV. Islam, MdKamrul, MdManjur Ahmed, and Kamal ZuhairiZamli. “Identifying the Pornographic Video on YouTube Using Vlog Stream.” 2018 4th International Conference on Computing Communication and Automation (ICCCA). IEEE, 2018.

V. J. Shayan, S. M. Abdullah and S. Karamizadeh, “An overview of objectionable image detection,” 2015 International Symposium on Technology Management and Emerging Technologies (ISTMET), Langkawi Island, 2015, pp. 396-400.doi: 10.1109/ISTMET.2015.7359066

VI. K. Zhou, L. Zhuo, Z. Geng, J. Zhang and X. G. Li, “Convolutional Neural Networks Based Pornographic Image Classification,” 2016 IEEE Second International Conference on Multimedia Big Data (BigMM), Taipei, 2016, pp. 206-209. doi: 10.1109/BigMM.2016.29

VII. L. Lv, C. Zhao, H. Lv, J. Shang, Y. Yang and J. Wang, “Pornographic images detection using High-Level Semantic features,” 2011 Seventh International Conference on Natural Computation, Shanghai, 2011, pp. 1015-1018. doi: 10.1109/ICNC.2011.6022151

VIII. M. B. Garcia, T. F. Revano, B. G. M. Habal, J. O. Contreras and J. B. R. Enriquez, “A Pornographic Image and Video Filtering Application Using Optimized Nudity Recognition and Detection Algorithm,” 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM), Baguio City, Philippines, 2018, pp. 1-5. doi: 10.1109/HNICEM.2018.8666227

IX. Moreira, Danilo&Fechine, Joseana. (2018). “A Machine Learning-based Forensic Discriminator of Pornographic and Bikini Images.” 1-8. 10.1109/IJCNN.2018.8489100.

X. Murugavalli, S., et al. “Enhancing security against hard AI problems in user authentication using CAPTCHA as graphical passwords.” International Journal of Advanced Computer Research 6.24 (2016): 93.

XI. MyoungBeom Chung, IlJuKo and DaeSik Jang, “Obscene image detection algorithm using high-and low-quality images,” 4th International Conference on New Trends in Information Science and Service Science, Gyeongju, 2010, pp. 522-527.

XII. Sheela, L. Jaba, V. Shanthi, and D. Jeba Singh. “Image mining using association rules derived from feature matrix.” Proceedings of the International Conference on Advances in Computing, Communication and Control. 2009.

XIII. Thenkalvi,B., and S. Murugavalli, “Image retrieval using certain block based difference of inverse probability and certain block based variation of local correlation coefficients integrated with wavelet moments.” Journal of Computer Science 10.8 (2014): 1497.

XIV. Y. Xu, B. Li, X. Xue and H. Lu, “Region-based Pornographic Image Detection,” 2005 IEEE 7th Workshop on Multimedia Signal Processing, Shanghai, 2005, pp. 1-4. doi: 10.1109/MMSP.2005.248675

XV. Yaqub, Waheeb&Mohanty, Manoranjan&Memon, Nasir. (2018). “Encrypted Domain Skin Tone Detection For Pornographic Image Filtering”. 1-5. 10.1109/AVSS.2018.8639350.

XVI. Zhang, J., Sui, L., Zhuo, L., & Li, Z. (2013). “Pornographic image region detection based on visual attention model in compressed domain”. IET Image Processing, 7, 384-391.

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Abstract:

A fluid is anything that moves, typically a liquid or even a gasoline, the last being differentiated through its own wonderful loved one compressibility. Liquids are treated as continual media, and also their movement and also condition can be defined in regards to the speed u, tension p, density, etc reviewed at every aspect in space x and also time t. To describe the density at a point, for example, expect the point to be bordered by an extremely tiny component (little compared to length ranges of passion in practices) which however contains a very large variety of molecules. The density is actually at that point the overall mass of all the particles in the aspect separated due to the quantity of the component.

Keywords:

CFD tool,Engineering applications,turbulence,

Refference:

I. Booker, J.R.: Thermal convection with absolutely temperature-dependent viscosity. J. Liquid Mech. 76 (4), pp. 741-754 (1976).

II. Bunge, H.P., Richards, M.A., Baumgardner, J.R.: Results of depth-dependent thickness on the platform of cover convection. Credit 379, pp. 436-438 (1996).

III. Burguete, J., Mokolobwiez, N., Daviaud, F., Garnier, N., Chiffaudel, A.: Buoyant-thermocapillary instabilities in significant coatings subjected to a straight temp slope. Phys. Fluids thirteen, pp. 2773-2787 (2001).

IV. Canuto, C., Hussaini, M.Y., Quarteroni, A., Zang, T.A. Spectral Approaches in Liquid Facet. Springer, Berlin (1988).

V. Daviaud, F., Vince, J.M.: Taking a trip surges in a fluid level subjected to a matching temperature incline. Phys. Rev. E 48, pp. 4432-4436 (1993).

VI. De Saedeleer, C., Garcimartin, A., Chavepeyer, G., Platten, J.K., Lebon, G.: The weakness of a liquefied level warmed up from the side when the higher place degrees to sky. Phys. Liquids 8( 3 ), pp. 670-676 (1996).

VII. D. Srinivasacharya, G. Swamy Reddy, “Heat and mass transfer by Natural convection in a doubly stratified porous medium saturated with Power-law fluid”, International Journal of Advanced Trends in Computer Applications, Vol.1 (1), pp.66–69, (2019).

VIII. G. Swamy Reddy, R. Archana Reddy, G.Ravi kiran ” A Review on computational Fluid Dynamics Projects”, Indian Journal of public health research and Development, Vol.9(11) (2018).

IX. G. Swamy Reddy, G. Ravi Kiran, R. Archana Reddy, “Radiation Impacts on Free Convection Circulation of a Power-Law Fluid past Vertical Plate Filled Along With Darcy Porous Medium” International Journal of Engineering and advanced Technology, Vol.8(6), pp.4582-4585, (2019).

X. G. Ravi Kiran, G. Swamy Reddy, B. Devika, R. Archana Reddy, “Effect Of Magnetic Field And Constriction On Pulsatile Flow Of a Dusty Fluid”, Journal Of Mechanics Of Continua And Mathematical Sciences, Vol.14 (6), pp.67–82, (2019).

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Abstract:

The symbolic residential or commercial property of liquids (both fluids and also gases) is composed in the ease with which they could be flawed. A suitable meaning of a fluid is not easy to condition as, in many instances, it is certainly not apparent to distinguish a fluid from a strong. In this training course we will definitely deal with "straightforward fluids", which Bachelor (1967) specifies as follows. "A simple fluid is actually a material such that the loved one positions of elements of the component modification by a volume which is certainly not little when suitable selected powers, however little in measurement, are actually related to the material. Particularly a basic fluid can easily certainly not stand up to any type of possibility by administered pressures to warp it in such a way which leaves the volume the same."

Keywords:

computational fluid dynamics,numerical methods,unsteady flow problems,

Refference:

I. B.C. Sim, A. Zebib. “Result of complimentary surface area coziness loss as well as likewise rotation on change to oscillatory thermocapillary convection.” Phys. Liquids 14 (1), 225 (2002).

II. B.C. Sim, A. Zebib, D. Schwabe. “Oscillatory thermocapillary convection in on call sphere annuli. Component 2. Likeness.”J. Fluid Mech. 491, 259 (2003).

III. D. Srinivasacharya, G. Swamy Reddy, “Heat and mass transfer by Natural convection in a doubly stratified porous medium saturated with Power-law fluid”, International Journal of Advanced Trends in Computer Applications, Vol.1 (1), pp.66–69, (2019).

IV. E. Favre, L. Blumenfeld and also F. Daviaud, “Weak point of a liquid finish regionally warmed up on its own absolutely free area.”Phys. Liquids 9, 1473 (1997).

V. G. Swamy Reddy, R. Archana Reddy, G. Ravi kiran ” A Review on computational Fluid Dynamics Projects”, Indian Journal of public health research and Development, Vol.9(11) (2018).

VI. G. Swamy Reddy, G. Ravi Kiran, R. Archana Reddy, “Radiation Impacts on Free Convection Circulation of a Power-Law Fluid past Vertical Plate Filled Along With Darcy Porous Medium” International Journal of Engineering and advanced Technology, Vol.8(6), pp.4582-4585, (2019).

VII. G. Ravi Kiran, G.Swamy Reddy, B. Devika, R.Archana Reddy, “EFFECT OF MAGNETIC FIELD AND CONSTRICTION ON PULSATILE FLOW OF A DUSTY FLUID” JOURNAL OF MECHANICS OF CONTINUA AND MATHEMATICAL SCIENCES, Vol.14 (6), pp.67–82, (2019).

VIII. M.A. Pelacho as well as J. Burguete, “Temperature oscillations of hydrothermal rises in thermocapillary-buoyancy convection.”Phys. Rev. E 59, 835 (1999).

IX. N. Garnier and also A. Chiffaudel. “2 perspective hydrothermal rises in an extended cylindrical vessel.” Eur. Phys. J. B 19, 87 (2001).

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Abstract:

Malicious insiders are executing the severe attacks on cloud by misusing their privileges, which leads to the irreversible damages and loss of reputation. As the malicious insiders are authorized and integral part of the cloud, detecting and obstructing them to prevent the cloud from malicious attacks, became the complex and instantly focusable research aspect. An efficient “Insider Threat Detection Model” was proposed using the behavior reliance anomaly detection process. This paper elucidates Behavior Reliance Insider Threat Detection Model (BRITDM) implementation process and an empirical study was also conducted on the proposed model. Amazon AWS modeled log file input records were used as input to detect the insider activities, using the proposed Behavior Reliance Anomaly Detection (BRAD) four layer architecture. Detailed user and admin activities were collected from the cloud log files that are represented in JSON format. JSQL Parser used for the query knowledge extraction and to create XML Tree. SVM classifier is trained with Compact Prediction Tree (CPT) structures knowledge starts with the comparison of admin executed activity query knowledge against the respective CPT structures of design level activity base, to determine whether the executed admin activity is malicious or not according to the BRAD four layered architecture. Cloud BRITDM processed 30 input records and resulted 5 as unique activities, 5 as abnormal, 2 as unintended suspicious activities and one as intended insider thereat and reaming are normal activities. Experimental results shown the proposed BRITDM performed well in identifying the unique, abnormal, and suspicious and threats from insider activities.

Keywords:

ITDM,BRAD Process flow,Anomaly Detection,Malicious Insider Threat Detection,

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