Archive

Abstract:

We examine the unsteady MHD free convective flow of a chemically reacting incompressible fluid over a vertical permeable plate under the influence of thermal radiation, Dufour and heat source/sink. The dimensionless governing equations are solved analytically using the three term perturbation method. Expressions for velocity, temperature and concentration for the flow are obtained and presented graphically. The analysis shows that Casson fluid parameter  increases the velocity; Dufour number increases the velocity and velocity; magnetic field force decreases the velocity; Chemical reaction rate increases the temperature but decreases the velocity and concentration; Grashof numbers increase the velocity when their values are increasingly varied. Furthermore, skin fiction coefficient, Nusselt number and Sherwood number for different values of governing parameters are calculated and the results are summarized in tabular form.

Keywords:

MHD,Casson fluid,Dufour effect,Free convection,Chemical reaction,

Refference:

I. Alao, F. I., A. I. Fagbade, and B. O. Falodun. “Effects of thermal radiation, Soret and Dufour on an unsteady heat and mass transfer flow of a chemically reacting fluid past a semi-infinite vertical plate with viscous dissipation.” Journal of the Nigerian mathematical Society 35.1 (2016): 142-158.

II. Charankumar, G., et al. “Chemical Reaction and Soret Effects on Casson MHD Fluid Flow over a Vertical Plate.” Int. J. Chem. Sci 14.1 (2016): 213-221.

III. Choudhary, Sunita, and MamtaGoyal. “Unsteady MhdCasson Fluid Flow Through Porous Medium With Heat Source/Sink And Time Dependent Suction.”Volume 56 Issue 6- April 2018.

IV. Falana, A., O. A. Ojewale, and T. B. Adeboje. “Effect of Brownian motion and thermophoresis on a nonlinearly stretching permeable sheet in a nanofluid.” Advances in Nanoparticles 5.01 (2016): 123.

V. Ferdows, M., MdJashimUddin, and A. A. Afify. “Scaling group transformation for MHD boundary layer free convective heat and mass transfer flow past a convectively heated nonlinear radiating stretching sheet.” International Journal of Heat and Mass Transfer 56.1-2 (2013): 181-187.

VI. Ibrahim, F. Hassanien, and A. Bakr. “Unsteady magnetohydrodynamic micropolar fluid flow and heat transfer over a vertical porous plate through a porous medium in the presence of thermal and mass diffusion with a constant heat source.” Canadian Journal of Physics 82.10 (2004): 775-790.

VII. Idowu, A. S., and B. O. Falodun. “Soret–Dufour effects on MHD heat and mass transfer of Walter’sB viscoelastic fluid over a semi-infinite vertical plate: spectral relaxation analysis.” Journal of Taibah University for Science 13.1 (2019): 49-62.

VIII. Kim, Youn J. “Heat and mass transfer in MHD micropolar flow over a vertical moving porous plate in a porous medium.” Transport in Porous Media 56.1 (2004): 17-37.

IX. Mahmud, Alam, and SattarAbdus. “Unsteady MHD free convection and mass transfer flow in a rotating system with Hall current, viscous dissipation and Joule heating.” Journal of Energy, Heat and Mass Transfer 22.2 (2000): 31-39.

X. Mohan, S. Rama, G. Viswanatha Reddy, and S. Balakrishna. “An Unsteady MHD Free Convection Flow of Casson Fluid Past an Exponentially Accelerated Infinite Vertical Plate through a Porous Media in the Presence of Thermal Radiation, Chemical Reaction and Heat Source or Sink” International Journal of Engineering and Techniques 4.4 (2018): 16-27.

XI. Narayana, PV Satya, B. Venkateswarlu, and S. Venkataramana. “Effects of Hall current and radiation absorption on MHD micropolar fluid in a rotating system.” Ain Shams Engineering Journal 4.4 (2013): 843-854.

XII. Ojjela, Odelu, and N. Naresh Kumar. “Unsteady MHD mixed convective flow of chemically reacting and radiating couple stress fluid in a porous medium between parallel plates with Soret and Dufour effects.” Arabian Journal for Science and Engineering 41.5 (2016): 1941-1953.

XIII. Okuyade, W. I. A., T. M. Abbey, and A. T. Gima-Laabel. “Unsteady MHD free convective chemically reacting fluid flow over a vertical plate with thermal radiation, Dufour, Soret and constant suction effects.” Alexandria engineering journal 57.4 (2018): 3863-3871.

XIV. Pal, Dulal, and BabulalTalukdar. “Perturbation technique for unsteady MHD mixed convection periodic flow, heat and mass transfer in micropolar fluid with chemical reaction in the presence of thermal radiation.” Open Physics 10.5 (2012): 1150-1167.

XV. Rajakumar, K. V. B., et al. “Radiation, dissipation and Dufour effects on MHD free convection Casson fluid flow through a vertical oscillatory porous plate with ion-slip current.” International Journal of Heat and Technology 36 (2018): 494-508.

XVI. Raju, M. C., N. Ananda Reddy, and S. V. K. Varma. “Analytical study of MHD free convective, dissipative boundary layer flow past a porous vertical surface in the presence of thermal radiation, chemical reaction and constant suction.” Ain Shams Engineering Journal 5.4 (2014): 1361-1369.

XVII. Sattar, Md, and MdMaleque. “Unsteady MHD natural convection flow along an accelerated porous plate with Hall current and mass transfer in a rotating porous medium.” Journal of Energy, Heat and Mass Transfer 22.2 (2000): 67-72.

XVIII. Seth, G. S., R. Sharma, and B. Kumbhakar. “Heat and Mass Transfer Effects on Unsteady MHD Natural Convection Flow of a Chemically Reactive and Radiating Fluid through a Porous Medium Past a Moving Vertical Plate with Arbitrary Ramped Temperature.” Journal of Applied Fluid Mechanics 9.1 (2016).

XIX. Seth, G. S., S. M. Hussain, and S. Sarkar. “Effects of Hall current and rotation on unsteady MHD natural convection flow with heat and mass transfer past an impulsively moving vertical plate in the presence of radiation and chemical reaction.” Bulgarian Chemical Communications 46.4 (2014): 704-718.

XX. Sharma, Bhupendra K., et al. “Soret and Dufour effects on unsteady MHD mixed convection flow past a radiative vertical porous plate embedded in a porous medium with chemical reaction.” Applied Mathematics 3.7 (2012): 717.

XXI. Srinivas, Suripeddi, ChallaKalyan Kumar, and AnalaSubramanyam Reddy. “Pulsating flow of Casson fluid in a porous channel with thermal radiation, chemical reaction and applied magnetic field.” Nonlinear Analysis: Modeling and Control 23.2 (2018): 213-233.

XXII. Ullah, Imran, Ilyas Khan, and SharidanShafie. “Soret and Dufour effects on unsteady mixed convection slip flow of Casson fluid over a nonlinearly stretching sheet with convective boundary condition.” Scientific reports 7.1 (2017): 1113.

XXIII. Vedavathi, N., et al. “Chemical Reaction, Radiation and Dufour effects on casson magneto hydro dynamics fluid flow over a vertical plate with heat source/sink.” Global Journal of Pure and Applied Mathematics 12.1 (2016): 191-200.

XXIV. Venkateswarlu, B., and P. V. SatyaNarayana. “Effects of thermal radiation on unsteady MHD micropolar fluid past a vertical porous plate in the presence of radiation absorption.” International Journal of Engineering Science and Computing 6.9 (2016).

XXV. Vijayaragavan, R. “Heat and Mass Transfer in Radiative Casson Fluid Flow Caused by a Vertical Plate with Variable Magnetic Field Effect.” Journal of Global Research in Mathematical Archives (JGRMA) 5.4 (2018): 48-66.

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Abstract:

Agriculture plays an important role in the Indian economy, therefore early prediction of plant diseases will help in increasing the productivity of crops thereby contributing to the economy’s growth. However, Manual identification of diseases in plants at every stage is very difficult since it involves huge manpower and requires extensive knowledge about plants. Multi disease patterns and pest identification can be automated using computer vision and deep learning techniques and by observing the controlled environmental parameters. Using, Internet of things the model can continuously monitor the temperature, humidity and water levels.

Keywords:

Computer Vision,Deep Learning,Segmentation,Classification,

Refference:

I. Humeau-Heurtier, “Texture Feature Extraction Methods: A Survey,” in IEEE Access, vol. 7, pp. 8975-9000, 2019. doi: 10.1109/ACCESS.2018.2890743.

II. AIP Conference Proceedings 2095, 030018 (2019);https://doi.org/10.1063/1.5097529. Published Online:09 April 2019.

III. Aitor Gutierrez, Ander Ansuategi, Loreto Susperregi, Carlos Tubío, Ivan Rankić, and Libor Lenža, “A Benchmarking of Learning Strategies for Pest Detection and Identification on Tomato Plants for Autonomous Scouting Robots Using Internal Databases,” Journal of Sensors, vol. 2019, Article ID 5219471, 15 pages, 2019.

IV. Akram, Tallha&Naqvi, Syed & Kamran, Muhammad & Kamran, Muhammad. (2017). Towards real-time crops surveillance for disease classification: exploiting parallelism in computer vision. Computers & Electrical Engineering. 59. 15-26. 10.1016/j.compeleceng.2017.02.020.

V. Arsenovic, M.; Karanovic, M.; Sladojevic, S.; Anderla, A.; Stefanovic, D. Solving Current Limitations of Deep Learning Based Approaches for Plant Disease Detection. Symmetry 2019, 11, 939.

VI. Azad, Dr&Hasan, Md& K, Mohammed. (2017). Color Image Processing on Digital Image. International Journal of New Technology and Research. 3. 56-62.

VII. Banchhor, C. &Srinivasu, N. 2018, “FCNB: Fuzzy Correlative Naive Bayes Classifier with MapReduce Framework for Big Data Classification”, Journal of Intelligent Systems.

VIII. Baranwal, Saraansh&Khandelwal, Siddhant&Arora, Anuja. (2019). Deep Learning Convolutional Neural Network for Apple Leaves Disease Detection. SSRN Electronic Journal. 10.2139/ssrn.3351641.

IX. B. Dhruv, N. Mittal and M. Modi, “Analysis of different filters for noise reduction in images,” 2017 Recent Developments in Control, Automation & Power Engineering (RDCAPE), Noida, 2017, pp. 410-415.

X. BOMMADEVARA, H.S.A., SOWMYA, Y. and PRADEEPINI, G., 2019. Heart disease prediction using machine learning algorithms. International Journal of Innovative Technology and Exploring Engineering, 8(5), pp. 270-272.

XI. Chandana, K., Prasanth, Y. &Prabhu Das, J. 2016, “A decision support system for predicting diabetic retinopathy using neural networks”, Journal of Theoretical and Applied Information Technology, vol. 88, no. 3, pp. 598-606.

XII. Chen, Jiansheng&Bai, Gaocheng& Liang, Shaoheng& Li, Zhengqin. (2016). Automatic Image Cropping: A Computational Complexity Study. 507-515. 10.1109/CVPR.2016.61.

XIII. Chouhan, Siddharth&Koul, Ajay & Singh, Dr. Uday& Jain, Sanjeev. (2018). Bacterial foraging optimization based Radial Basis Function Neural Network (BRBFNN) for identification and classification of plant leaf diseases: An automatic approach towards Plant Pathology. IEEE Aceess.

XIV. Chouhan, Siddharth& Singh, Dr. Uday& Jain, Sanjeev. (2019). Applications of Computer Vision in Plant Pathology: A Survey. Archives of Computational Methods in Engineering. 10.1007/s11831-019-09324-0.

XV. Dey, Abhishek&Bhoumik, Debasmita&Dey, Kashi. (2019). Automatic Multi-class Classification of Beetle Pest Using Statistical Feature Extraction and Support Vector Machine: Proceedings of IEMIS 2018, Volume 2. 10.1007/978-981-13-1498-8_47.

XVI. Ferreira, Alessandro &Freitas, Daniel & Silva, Gercina&Pistori, Hemerson&Folhes, Marcelo. (2017). Weed detection in soybean crops using ConvNets. Computers and Electronics in Agriculture. 143. 314-324. 10.1016/j.compag.2017.10.027.

XVII. Hassanien, Aboul Ella &Gaber, Tarek&Mokhtar, Usama&Hefny, Hesham. (2017). An improved moth flame optimization algorithm based on rough sets for tomato diseases detection. Computers and Electronics in Agriculture. 136. 86-96. 10.1016/j.compag.2017.02.026.

XVIII. Hossain, Eftekhar&Hossain, Md&Rahaman, Mohammad. (2019). A Color and Texture Based Approach for the Detection and Classification of Plant Leaf Disease Using KNN Classifier. 1-6. 10.1109/ECACE.2019.8679247.

XIX. I. M. Krishna, C. Narasimham and T. B. Reddy, “Image super resolution and contrast enhancement using curvlet’s with cycle spinning,” 2016 International Conference on Communication and Electronics Systems (ICCES), Coimbatore, 2016, pp. 1-6. doi: 10.1109/CESYS.2016.7889926.

XX. I.Murali Krishna, Dr. ChallaNarsimham and Dr.A.S.N. Chakravarthy Published a paper on ” A Novel Feature Selection based Classification Model for Disease Severity Prediction on Alzheimer’s Database”,2018,JARDCS,Volume-10,Issue-4 Page no: 245-255 ISSN: 1943023X.

XXI. Jha, Kirtan&Doshi, Aalap& Patel, Poojan& Shah, Manan. (2019). A comprehensive review on automation in agriculture using artificial intelligence. Artificial Intelligence in Agriculture. 2. 10.1016/j.aiia.2019.05.004.

XXII. Kaur, Sukhvir&Pandey, Shreelekha&Goel, Shivani. (2018). Plants Disease Identification and Classification Through Leaf Images: A Survey. Archives of Computational Methods in Engineering. 26. 10.1007/s11831-018-9255-6.

XXIII. Kiani, Ehsan&Mamedov, Tofik. (2017). Identification of plant disease infection using soft-computing: Application to modern botany. Procedia Computer Science. 120. 893-900. 10.1016/j.procs.2017.11.323.

XXIV. Kishore, P.V.V., Kumar, K.V.V., Kiran Kumar, E., Sastry, A.S.C.S., TejaKiran, M., Anil Kumar, D. & Prasad, M.V.D. 2018, “Indian Classical Dance Action Identification and Classification with Convolutional Neural Networks”, Advances in Multimedia, vol. 2018.

XXV. Konstantinos P. Ferentinos, Deep learning models for plant disease detection and diagnosis, Computers and Electronics in Agriculture, Volume 145, 2018, Pages 311-318, ISSN 0168-1699, https://doi.org/10.1016/j.compag.2018.01.009.

XXVI. Kour, Vippon&Arora, Sakshi. (2019). Fruit Disease Detection Using Rule-Based Classification: Proceedings of ICSICCS-2018. 10.1007/978-981-13-2414-7_28.

XXVII. Lu, Yang & Yi, Shujuan&Zeng, Nianyin& Liu, Yurong& Zhang, Yong. (2017). Identification of Rice Diseases using Deep Convolutional Neural Networks. Neurocomputing. 267. 10.1016/j.neucom.2017.06.023.

XXVIII. Ma, Juncheng& Du, Keming&Zheng, Feixiang& Zhang, Lingxian& Sun, Zhongfu. (2018). A Segmentation Method for Processing Greenhouse Vegetable Foliar Disease Symptom Images. Information Processing in Agriculture. 6. 10.1016/j.inpa.2018.08.010.

XXIX. Mondal, Dhiman&Kole, Dipak& Roy, Kusal. (2017). Gradation of yellow mosaic virus disease of okra and bitter gourd based on entropy based binning and Naive Bayes classifier after identification of leaves. Computers and Electronics in Agriculture. 142. 10.1016/j.compag.2017.11.024.

XXX. M. Sardogan, A. Tuncer and Y. Ozen, “Plant Leaf Disease Detection and Classification Based on CNN with LVQ Algorithm,” 2018 3rd International Conference on Computer Science and Engineering (UBMK), Sarajevo, 2018, pp. 382-385. doi: 10.1109/UBMK.2018.8566635.

XXXI. Narmadha, R. &Arulvadivu, G.. (2017). Detection and measurement of paddy leaf disease symptoms using image processing. 1-4. 10.1109/ICCCI.2017.8117730.

XXXII. Narottambhai, Mitisha&Tandel, Purvi. (2016). A Survey on Feature Extraction Techniques for Shape based Object Recognition. International Journal of Computer Applications.137.16-20.10.5120/ijca2016908782.

XXXIII. PadmajaGrandhe, Dr. E. Sreenivasa Reddy, Dr.D.Vasumathi . (2016). An Adaptive Cluster Based Image Search And Retrieve For Interactive Roi To Mri Image Filtering, Segmentation, And Registration (Vol. 94,. No.1). Journal of Theoretical and Applied Information Technology.

XXXIV. Pantazi, X. E., Moshou, D., &Tamouridou, A. A. (2019). Automated leaf disease detection in different crop species through image features analysis and One Class Classifiers. Computers and Electronics in Agriculture, 156, 96–104.

XXXV. Pearline, Anubha& Kumar, Sathiesh&Harini, S.. (2019). A study on plant recognition using conventional image processing and deep learning approaches. Journal of Intelligent & Fuzzy Systems. 36. 1-8. 10.3233/JIFS-169911.

XXXVI. Rahman, Ziaur& PU, Yi-Fei&Aamir, Muhammad &Ullah, Farhan. (2018). A framework for fast automatic image cropping based on deep saliency map detection and gaussian filter. International Journal of Computers and Applications. 1-11. 10.1080/1206212X.2017.1422358.

XXXVII. R. Gandhi, S. Nimbalkar, N. Yelamanchili and S. Ponkshe, “Plant disease detection using CNNs and GANs as an augmentative approach,” 2018 IEEE International Conference on Innovative Research and Development (ICIRD), Bangkok, 2018, pp. 1-5. doi: 10.1109/ICIRD.2018.8376321.

XXXVIII. Sandhu, Gittaly& Kumar, Vinay& Joshi, Hemdutt. (2017). Study of digital image processing techniques for leaf disease detection and classification. Multimedia Tools and Applications. 1-50. 10.1007/s11042-017-54458.

XXXIX. Shanwen Zhang, Wenzhun Huang, Chuanlei Zhang, Three-channel convolutional neural networks for vegetable leaf disease recognition, Cognitive Systems Research, Volume 53, 2019, Pages 31-41, ISSN 1389-0417, https://doi.org/10.1016/j.cogsys.2018.04.006.

XL. Shuli, Xing & Lee, Marely& Lee, Keun-kwang. (2019). Citrus Pests and Diseases Recognition Model Using Weakly Dense Connected Convolution Network. Sensors. 19. 3195. 10.3390/s19143195.

XLI. Tetila, Everton & Machado, Bruno &Belete, Nícolas Alessandro &Guimaraes, David &Pistori, Hemerson. (2017). Identification of Soybean Foliar Diseases Using Unmanned Aerial Vehicle Images. IEEE Geoscience and Remote Sensing Letters. PP. 1-5. 10.1109/LGRS.2017.2743715.

XLII. Thangaiyan, Jayasankar. (2019). AN IDENTIFICATION OF CROP DISEASE USING IMAGE SEGMENTATION. 10.13040/IJPSR.0975-8232.10(3).1054-64.

XLIII. Yuheng, Song &Hao, Yan. (2017). Image Segmentation Algorithms Overview.

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Abstract:

The finite element (FE) solutions are different from the exact ones due to the presence of various error sources, such as computer round-off error, error due to discrete of the displacement field, etc. This paper uses the h- and p-refinement of the finite element method for the laser butt weld problem, with the base metal is AISI 1018 steel highness 8 mm. The objective is to present estimation techniques the strain energy relative error and evaluate its reliability through two indices: the affectivity index and the uniformity index SD. The numerical results achieve to meet the conditions for reliability assessment. Specifically, the, , SD values of h- refinement, and p- refinement respectively: less than 6%, 0.535667, 0.019528, and less than 4%, 0.506616, 0.103834.

Keywords:

Finite element method (FEM),Laser butt weld,Relative error,Reliability,h- refinement,p- refinement,

Refference:

I. A. Düster & E. Rank, “The p-version of the finite element method compared to an adaptive h-version for the deformation theory of plasticity”, Computer Methods in Applied Mechanics and Engineering, 190(15-17), 1925–1935, 2001 (https://doi.org/10.1016/s0045-7825(00)00215-2)
II. B. A. Szabó, “Mesh design for the p-version of the finite element method”, Computer Methods in Applied Mechanics and Engineering, 55(1-2), 181–197 (https://doi.org/10.1016/0045-7825(86)90091-5), 1986
III. B. A. Szabo, P. K. Basu, and M. P. Rossow, “Adaptive Finite Element Analysis Based on P-Convergence”, Symposium on Future Trends in Computerized Structural Analysis and Synthesis, Washington, D.C., NASA Conference Publication 2059, pp. 43-50, 1978
IV. Claudio Canuto, Ricardo H. Nochetto, Rob P. Stevenson, and Marco Verani, “Convergence and Optimality of hp-AFEM”, Numer. Math. 135, 1073–1119 (https://doi.org/10.1007/s00211-016-0826-x), 2017
V. F.Cugnon & P.Beckers, “Error estimation for h- and p-methods”, 8th Mechanical Engineering Chilean Congress”, Concepción, pp.183-188, 1998
VI. F.Cugnon: Automatisation des calculs elements finis dans le cadre de la methode-p, these de doctorat, ULG, 2000
VII. F.Cugnon, M. Meyers, P.Beckers& G. Warzee, “Iterative solvers for the p-version of the finite element method”, first international conference on Advanced Computational Methods in Engineering – ACOMEN’ 98, Ghent, pp. 737-744, 1988
VIII. I. Babuška and B. A. Szabó,“On the Rates of Convergence of the Finite Element Method”, Int. J. Numer. Meth. Engng., 18, 323-341, 1982
IX. I. Babuška and W.C. Rheinboldt, “A‐posteriori error estimates for the finite element method”, Int. J. Numer. Meth. Engng, 12: 1597-1615, 1978 (http://dx.doi.org/10.1002/nme.1620121010)
X. Information on http://amet-me.mnsu.edu/ UserFilesShared/ DATA_ACQUISITION/mts/MaterialData/MaterialData_6809-1018ColdDrawn.pdf
XI. Information on http://homepages.engineering.auckland.ac.nz /~pkel015/ SolidMechanicsBooks/Part_I/BookSM_Part_I/08_Energy/08_Energy_02_Elastic_Strain_Energy.pdf
XII. Information on http://www.engr.mun.ca/~katna/5931/ STRAIN%20 ENERGY-Impact Loading.pdf
XIII. Information on http://www.yandreou.com/wp-content/uploads/2014/08/AISI-1018-Mild-Low-Carbon-Steel-PDF.pdf
XIV. J. E. Flaherty: Finite element analysis, Troy, New York, 2002
XV. Jae S. Ahn, Seung H. Yang, and Kwang S. Woo.,“Free Vibration Analysis of Patch Repaired Plates with a Through Crack by p-Convergent Layer-wise Element”, The Scientific World Journal, 2014. Article ID 427879. (http://dx.doi.org/10.1155/2014/427879)
XVI. L. Demkowicz, Ph. Devloo, J.T. Oden,“On an h-type mesh-refinement strategy based on minimization of interpolation errors”, Computer Methods in Applied Mechanics and Engineering, Volume 53, Issue 1, Pages 67-89, ISSN 0045-7825, 1985 (https://doi.org/10.1016/0045-7825(85)90076-3)
XVII. Michael R. Dörfel, Bert Jüttler, Bernd Simeon, “Adaptive isogeometric analysis by local h-refinement with T-splines”, Computer Methods in Applied Mechanics and Engineering, Volume 199, Issues 5–8, Pages 264-275, ISSN 0045-7825, 2010 (https://doi.org/10.1016/j.cma.2008.07.012)
XVIII. Son. Nguyen,“The error estimate and the convergence rate for h, p – refinement in the Finite Element Analysis”. Vietnam Journal of Mechanics, VAST, Vol. 30, No. 3, pp. 179 – 184, 2008 (https://doi.org/10.15625/0866-7136/30/3/5617)
XIX. Stephan, Ernst &Wriggers, Peter,“Modeling, Simulation, and Software Concepts for Scientific-Technological Problems”, 2011 (https://doi.org/10.1007/978-3-642-20490-6)
XX. Y. L. Kuo, W. L. Cleghorn, K. Behdinan, & R. G. Fenton, “The h–p–r-refinement finite element analysis of a planar high-speed four-bar mechanism”, Mechanism and Machine Theory, 41(5), 505–524, 2006 (https://doi.org/10.1016/j.mechmachtheory.2005.09.001)\

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Abstract:

In this article, we discuss some functional identities of certain semirings which enable us to induce commutativitiy in them. This will be helpful to extend some remarkable results of ring theory in the canvas of semirings. We also study some other useful functional identities which are trivial in ordinary rings.

Keywords:

Semiring,Inversesemiring,MA-semiring,Derivation,

Refference:

I. Bell H. E., DaifM. N., “On derivation and commutativity in prime rings”,ActaMathematicaHungarica, vol. 66,pp: 337-343, 1995.

II. Bistarelli S., MontanariU.and Rossi F., “Semiring-based constraint logic programming:syntax and semantics”,ACMTransactions on Programming Languages and Systems, vol. 23, pp:1-29, 2001.

III. Glazek, A., Guide to the Literature on Semirings and their Applications in Mathematics and Information Sciences,Kluwer, Dordrecht, 2000.

IV. Golan J. S., Semirings and Affine Equations over Them: Theory and Applications, Kluwer, Dordrecht, 2003.

V. HersteinI. N., “A note on derivations”,Canadian Mathematical Society,vol.21, pp:369-370, 1978.

VI. Javed M. A., AslamM., HussainM., “On condition (A2) of Bandletand Petrich for inverse semirings”,International Mathematical Forum,vol.59, pp: 2903-2914, 2012.

VII. Javed M.A., AslamM., “Some commutativity conditions in prime MA-semirings”, Ars Combinatoriavol.114,pp:373-384, 2014.

VIII. Pouly M. “Generic solution construction in valuation-based systems.Advances in Artificial Intelligence”,vol.6657, pp: 335-346, 2011.

IX. Karvellas P.H., “Inversivesemirings”, Journal of the Australian Mathematical Society, vol: 18, pp: 277-287,1974.

X. Posner E. C., “Derivations in prime rings”, Proceedings of the American Mathematical Society, vol. 8, pp:1093-1100, 1957.

XI. Vandiver H. S., “Note on a simple type of algebra in which cancellation law of addition does not hold”,Bulletin of The American Mathematical Society, vol. 40,pp: 914-920, 1934.

XII. VukmanJ., “Commutating and centralizing mappings in primerings”, Proceedings of the American Mathematical Society, vol.109,pp: 47-52, 1990.

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Abstract:

This paper applies Modifed Decomposition Method (MDM) as numerical analysis linear second-order FredholmIntegro-differential Equations. The calculation of the approximate solutions are computed by mathematical package. The main aim of this paper is to demonstrate how effective this method minimizes the size of calculations and reaching the final solution in the shortest time and best result. When com paring the results with the (ADM) and with the exact solution, we will note how effective this method minimizes the size of calculations of the solution and reaches the exact solution. Accordingly, the (MDM) is the best method to be used to solve linear second-order FredholmIntegro-Differential equation. The convertion to the exact solution is notably fast and also a time saver, as it requires less computational work in solving equations. This is why the (MDM) is more efficient in solving this kind of equations.

Keywords:

MDM,Integro-differential Equations,Fredholm integral Equation,approximate solutions,

Refference:

I. A. Mohsen, M. El-Gamel. “A Sinc–Collocation method for the linear Fredholmintegro-differential equations”, in Zeits chrift fürangewandte Mathematik und Physik, pp.380-390, 2007.

II. A.M Wazwaz, “A reliable modification of Adomian decomposition method”, in Applied Mathematics and Computation, pp.77-86, 1999.

III. D.D Bainov, M.B Dimitrova, A.B Dishliev, “Oscillation of the bounded solutions of impulsive differential-difference equations. of second order”, in Applied Mathematics and Computation, pp.61-68, 2000.

IV. E. Aruchunan, J. Sulaiman, “Numerical Solution of First-Order Linear FredholmIntegro-Differential Equations using Conjugate Gradient Method”, in International Symposium on Geology, pp.11-13, 2009.

V. E. Aruchunan, J. Sulaiman, “Numerical solution of second-order linear fredholmintegro-differential equation using generalized minimal residual method”, in American Journal of Applied Science, pp.780-783,2010.

VI. H. Safdari, Y.E Aghdam, “Numerical Solution of Second-Order Linear FredholmIntegro-Differetial Equations by Trigonometric Scaling Functions”, in Open Journal of Applied Sciences, pp.135-144, 2015.

VII. M. Gülsu, M. Sezer, “A Taylor polynomial approach for solving differential-difference equations”, in Journal of Computational and Applied Mathematics, pp.349-364, 2006.

VIII. M. Fathy, M. El-Gamel, M.S El-Azab, “Legendre–Galerkin method for the linear Fredholmintegro-differential equations”, in Applied Mathematics and Computation, pp.789-800, 2014.

IX. M.FKarim, M. Mohamad, M.S Rusiman, N. Che-Him, R.Roslan, K. Khalid, “ADM For Solving Linear Second-Order FredholmIntegro-Differential Equations”, in Journal of Physics: Conference Series, pp.012009, 2018.

X. S. Yalçinbaş, M. Sezer,“The approximate solution of high-order linear Volterra–Fredholmintegro-differential equations in terms of Taylor polynomials”,in Applied Mathematics and Computation, pp.291-308, 2000.

XI. S.M Hosseini, S. Shahmorad,“Tau numerical solution of Fredholmintegro-differential equations with arbitrary polynomial bases”, in Applied Mathematical Modelling,pp.145-154, 2003.

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Abstract:

An effort has been prepared numerically to investigate thermophoresis and diffusion thermo effects on liquid motion past a permeable surface. Motion is managed by the constitutive equation of power law fluid model. External forces appeared in the flow system are Lorentz force due to external magnetic field, buoyancy force. Similarity transformation has been utilized in the methodology part and MATLAB built in bvp4c solver scheme has been adopted to carry out the numerical solutions. Impacts of flow parameters on flow characteristics have been outlined by figures and diagrams.

Keywords:

MHD,Power-law fluid,Soret Effect (thermophoresis),Dufoureffect (diffusion thermo),thermal and mass transfer,

Refference:

I. Acrious, A., Shah, M.J., Peterson E.E., “Momentum and heat transfer in laminar boundary layer flow on non-newtonian fluids past external surfaces”, AIChE Journal. vol. 6, pp: 312–316, 1960.
II. Andersson, H.I., Bech, K.H. and Dandapat, B.S., “Magnetohydrodynamic flow of a power law fluid over a stretching sheet”, International Journal of Non-Linear Mechanics. vol. 72, pp: 929–936, 1992
III. Aziz, A., Ali, Y., Aziz, T. and Siddique, J., “Heat Transfer analysis for stationary boundary layer slip flow of a power low fluid in a Darcy porous medium with plate suction/injection”, PLoS ONE. Vol.10 (9), doi:10.1371/journal.pone.0138855, 2015
IV. Cheng, C.Y., “Soret and Dufour effects on mixed convection heat and mass transfer from a vertical wedge in a porous medium with constant wall temperature and concentration”, Transport in Porous Media. vol. 94, pp: 123–32, 2012.
V. Dey, D., “Non-Newtonian effects on hydromagnetic dusty stratified fluid flow through a porous medium with volume fraction”, Proceedings of the National Academy of Sciences, India Section A: Physical Sciences, 86(1), pp: 47-56, 2016.
VI. Hirschhorn J., Madsen M., Mastroberardino A. and Siddique J.I., “Magnetohydrodynamic boundary layer slip flow and heat transfer of power-law fluid over a flat plate”, Journal of Applied Fluid Mechanics, 9(1), pp: 11-17, 2016.
VII. Huang, C.J. and Yih, K.A., “Heat and Mass Transfer on the Mixed Convection of non-Newtonian fluids over a vertical wedge with Soret/Dufour effects and Internal Heat Generation: Variable wall Temperature/Concentration”, Transport in Porous Media. vol.130, pp: 559-576, 2019.
VIII. Jafarimughaddam, A. and Aberoumand, S., “Exact approximations for skin friction coefficient and convective heat transfer coefficient for a class of power-law fluids flow over a semi-infinite plate: Results from similarity solution”, Engineering Science and Technology: an International Journal. vol. 20(3), pp: 1115-1121, 2016.
IX. Lee, S.Y., Ames, W.F., “Similar solutions for non-Newtonian fluids”, AIChE Journal. vol. 12, pp: 700–708, 1960.
X. Saritha, K., Rajasekhar, M.N. and Reddy, B.S. “Combined effects of soret and dufour on mhd flow of a Power-law fluid over flat plate in slip flow regime”, International Journal of Applied Mechanics and Engineering. vol. 23(3), pp: 689-705, 2018
XI. Schowalter, W.R., “The application of boundary layer theory to power law pseudo plastic fluids: similar solutions”, AIChE Journal. vol. 6(1), pp: 24-28, 1960.
XII. Sharma, B.K., Gupta, S., Vamsikrishna, V. and Bhargavi, R.J., “Soret and Dufour effects on an unsteady MHD mixed convective flow past an infinite vertical plate with Ohmic dissipation and heat source”, AfrikaMathematika, vol. 25, pp. 799–821, 2014.
XIII. Shateyi, S., Motsa, S.S. and Sibanda, P., “The effects of thermal radiation, Hall currents, Soret and Dufour on MHD flow by mixed convection over a vertical surface in porous media”, Mathematical Problems in Engineering, Article ID 627475, 2010
XIV. Tai, B.C. and Char, I.M. “Soret and Dufour effects on free convection flow of non-Newtonian fluids along a vertical plate embedded in a porous medium with thermal radiation”, International Communications in Heat and Mass Transfer. vol. 37, pp. 480-483, 2010
XV. Zhang, H., Kang, Y., and Xu, T., “Study on Heat Transfer of non-Newtonian Power-law fluid in pipes with different cross sections”, Procedia Engineering. vol. 205, pp. 3381-3388, 2017.

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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.
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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.

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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.

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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.
IX. H. Junker, O. Amft, P. Lukowicz, and G. Tro¨ster. Gesture spotting with body-worn inertial sensors to detect user activities. Pattern Recognition, 41(6):2010–2024, 2008. Bakharev T., “Durability of geopolymer materials in sodium and magnesium sulfate solutions”, Cement and Concrete Research. vol. 35, no. 6, pp: 1233-1246, 2005
X. J. Fessler and B. Sutton. Nonuniform fast fourier transforms using min-max interpolation. IEEE Trans. Signal Process., 51(2):560–574, 2003
XI. L. Gao, A. K. Bourke, and J. Nelson. Evaluation of accelerometer based multi-sensor versus single-sensor activity recognition systems. Med. Eng. Phys.,36(6):779–785, 2014.
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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XIV. M. G o¨vercin, Y. Ko¨ltzsch, M. Meis, S. Wegel,M. Gietzelt, J. Spehr, S. Winkelbach, M. Marschollek, and E. Steinhagen-Thiessen. Defining the user requirements for wearable and optical fall prediction and fall detection devices for home use. Inform. Health Soc. Care, 35(3-4):177–187, 2010.
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XX. Shinmoto Torres, R. L., Ranasinghe, D. C., Shi, Q. (2013, December). Evaluation of wearable sensor tag data segmentation approaches for real time activity classification in elderly. In International Conference on Mobile and Ubiquitous Systems: Computing, Networking, and Services (pp. 384-395). Springer International Publishing.
XXI. Shinmoto Torres, R. L., Ranasinghe, D. C., Shi, Q., Sample, A. P. (2013, April). Sensor enabled wearable RFID technology for mitigating the risk of falls near beds. In 2013 IEEE International Conference on RFID (pp.191-198). IEEE.
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.
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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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