Archive

Abstract:

Currently, COVID-19 is the biggest obstacle for the survival of the human race. Again, as mobile technology is now an essential component of human life, hence it is possible to utilize the power of mobile technology against the treat of COVID-19. Every nation is now trying to deploy an interactive platform for creating public awareness and share the necessary information related to COVID-19. Keeping all of these in mind, authors have deployed an interactive cross-platform (web/mobile) application GO-COVID for the ease of the users, specifically in India. This dashboard is featured with all the real-time attributes regarding the novel coronavirus disease and its measures and controls. The system deliberately aims to maintain the digital well-being of the society, create public awareness, and not create any panic situation among the individuals of the society. The application uses modern AI-ML tools to analyze the disease among the individuals with the help of an informative test and has also deployed a chat-bot for user ease of interaction. The application also collects the geo-location and other necessary historical data to ensure your safety and distancing from the affected personals. The same is also used to backtrack the ones affected and perform tests. All of these features enable the app to compete with the pandemic in this modern world.

Keywords:

COVID-19,pneumonia,mobile application,Artificial Intelligence-Machine Learning (AI-ML) tool,chat-bot,geo-location,

Refference:

I. A. Martin, J. Nateqi, S. Gruarin, N. Munsch, I. Abdarahmane, & B.Knapp, “An artificial intelligence-based first-line defence against COVID-19: digitally screening citizens for risks via a chatbot”. bioRxiv, 2020

II. A. Chakraborty, J.S. Banerjee, A. Chattopadhyay, Malicious node restricted quantized data fusion scheme for trustworthy spectrum sensing in cognitive radio networks. Journal of Mechanics of Continua and Mathematical Sciences,15(1), 39–56, 2020

III. A.Chakraborty, and J.S.Banerjee, “An Advance Q Learning (AQL) Approach for Path Planning and Obstacle Avoidance of a Mobile Robot”. International Journal of Intelligent Mechatronics and Robotics, 3(1), pp 53-73 2013

IV. A.Chakraborty, J. S. Banerjee, and A.Chattopadhyay, “Non-Uniform Quantized Data Fusion Rule Alleviating Control Channel Overhead for Cooperative Spectrum Sensing in Cognitive Radio Networks”. In: Proc. IACC, pp 210-215 2017

V. A.Chakraborty, J. S. Banerjee, and A.Chattopadhyay, “Non-uniform quantized data fusion rule for data rate saving and reducing control channel overhead for cooperative spectrum sensing in cognitive radio networks”,Wireless Personal Communications,Springer, 104(2), 837-851, 2019

VI. D. Das, et. al., “Analysis of Implementation Factors of 3D Printer: The Key Enabling Technology for making Prototypes of the Engineering Design and Manufacturing”, International Journal of Computer Applications, pp.8-14, 2017

VII. D. Das, et. al., “An in-depth Study of Implementation Issues of 3D Printer”, in Proc. MICRO 2016 Conference on Microelectronics, Circuits and Systems (pp. 45-49), 2016

VIII. E.Dong, H.Du, &L. Gardner, “An interactive web-based dashboard to track COVID-19 in real time”. The Lancet infectious diseases, 2020

IX. F.Andry, L. Wan, D. Nicholson, “A mobile application accessing patients’ health records through a rest API”, In Proceedings of the 4th International Conference, scitepress.org, 2011

X. H. L. Semigran, J. A. Linder, C. Gidengil, & A. Mehrotra, “Evaluation of symptom checkers for self diagnosis and triage: audit study”. bmj, 351, h3480, 2015

XI. https://www.bing.com/covid

XII. https://covid.apollo247.com/

XIII. https://covindia.com/

XIV. https://www.mygov.in/aarogya-setu-app/

XV. I. Pandey, et. al., “WBAN: A Smart Approach to Next Generation e-healthcare System”, In 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC), pp. 344-349, IEEE, 2019

XVI. J. Luo, “Mobile computing in healthcare: the dreams and wishes of clinicians”. In Proceedings of the 2nd International Workshop on Systems and Networking Support for Health Care and Assisted Living Environments (pp. 1-4), 2008

XVII. J. Banerjee, et. al., “Impact of machine learning in various network security applications”, In 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC), pp. 276-281, IEEE, 2019

XVIII. J. Chattopadhyay, S. Kundu, A. Chakraborty, J. S.Banerjee, “Facial expression recognition for human computer interaction”, in Proceedings of ICCVBIC 2018, Springer (press), 2020

XIX. J. S. Banerjee,A.Chakraborty, and A.Chattopadhyay,“Relay node selection using analytical hierarchy process (AHP) for secondary transmission in multi-user cooperative cognitive radio systems”, in Proc. ETAEERE 2016, LNEE-Springer, Dec. 2016

XX. J. S. Banerjee,A.Chakraborty, and A.Chattopadhyay,“Fuzzy based relay selection for secondary transmission in cooperative cognitive radio networks”, in Proc. OPTRONIX 2016, Springer, India, Aug. 2016

XXI. J. S. Banerjee,A.Chakraborty, and A.Chattopadhyay,“Reliable best-relay selection for secondary transmission in co-operation based cognitive radio systems: A multi-criteria approach”, Journal of Mechanics of Continua and Mathematical Sciences, 13(2), 24-42, 2018

XXII. J. S. Banerjee, and A. Chakraborty, “Fundamentals of Software Defined Radio and Cooperative Spectrum Sensing: A Step Ahead of Cognitive Radio Networks”. In Handbook of Research on Software-Defined and Cognitive Radio Technologies for Dynamic Spectrum Management, IGI Global, pp 499-543 2015

XXIII. J.S.Banerjee, A.Chakraborty, and K.Karmakar, “Architecture of Cognitive Radio Networks”. In N. Meghanathan& Y.B.Reddy (Ed.), Cognitive Radio Technology Applications for Wireless and Mobile Ad Hoc Networks, IGI Global, pp 125-152 2013

XXIV. J. S. Banerjee, and A.Chakraborty, “Modeling of Software Defined Radio Architecture & Cognitive Radio, the Next Generation Dynamic and Smart Spectrum Access Technology”. In M.H. Rehmani& Y. Faheem (Ed.), Cognitive Radio Sensor Networks: Applications, Architectures, and Challenges, IGI Global, pp. 127-158 2014

XXV. J.S.Banerjee, et. al., “A Comparative Study on Cognitive Radio Implementation Issues”, International Journal of Computer Applications, vol.45, no.15, pp. 44-51, May.2012

XXVI. J. S. Banerjee,A.Chakraborty, and A.Chattopadhyay, “A novel best relay selection protocol for cooperative cognitive radio systems using fuzzy AHP”, Journal of Mechanics of Continua and Mathematical Sciences, 13(2), 72-87, 2018

XXVII. J. S. Banerjee, D. Goswami, and S. Nandi, “OPNET: a new paradigm for simulation of advanced communication systems”, in Proc. International Conference on Contemporary Challenges in Management, Technology & Social Sciences, SEMS, India, (pp. 319-328), 2014

XXVIII. J. S. Banerjee, et. al., “A Survey on Agri-Crisis in India Based on Engineering Aspects”, Int. J. of Data Modeling and Knowledge Management, 3(1–2), pp.71-76, 2013

XXIX. K. Karmakar, J.S. Banerjee, “Different network micro-mobility protocols and their performance analysis”. Int. J. Comput. Sci. Inf. Technol. 2(5), 2165–2175, 2011

XXX. J.Kaminski, “Informatics in the time of COVID-19”, 2020

XXXI. M. N. K. Boulos, & E. M. Geraghty, “Geographical tracking and mapping of coronavirus disease COVID-19/severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic and associated events around the world: how 21st century GIS technologies are supporting the global fight against outbreaks and epidemics”, Int J Health Geogr 19, 8 , 2020

XXXII. M. Cascella, M. Rajnik, A. Cuomo, S. C. Dulebohn, & R. Di Napoli, “Features, evaluation and treatment coronavirus (COVID-19)”. In Statpearls [internet]. StatPearls Publishing, 2020

XXXIII. O.Saha; A. Chakraborty, and J. S. Banerjee, “A Decision Framework of IT-Based Stream Selection Using Analytical Hierarchy Process (AHP) for Admission in Technical Institutions”, In: Proc. OPTRONIX 2017, IEEE, pp. 1-6, Nov. 2017

XXXIV. O. Saha; A. Chakraborty, and J. S. Banerjee, “A Fuzzy AHP Approach to IT-Based Stream Selection for Admission in Technical Institutions in India”, In: Proc. IEMIS, AISC-Springer, pp. 847-858, 2019

XXXV. R. Roy, S.Dutta, S. Biswas, & J. S. Banerjee, “Android Things: A Comprehensive Solution from Things to Smart Display and Speaker”. In Proceedings of International Conference on IoT Inclusive Life (ICIIL 2019), NITTTR Chandigarh, India, 339-352, Springer, Singapore,2020

XXXVI. S. Paul, A. Chakraborty, and J. S. Banerjee, “A Fuzzy AHP-Based Relay Node Selection Protocol for Wireless Body Area Networks (WBAN)”, In: Proc. OPTRONIX 2017, IEEE, pp. 1-6, Nov. 2017

XXXVII. S. Paul, A. Chakraborty, and J. S. Banerjee, “The Extent Analysis Based Fuzzy AHP Approach for Relay Selection in WBAN”, In: Proc. CISC, (pp. 331-341). Springer, Singapore, 2019

XXXVIII. S. Guhathakurata, S. Kundu, A. Chakraborty, J. S.Banerjee, “A Novel Approach to Predict COVID-19 Using Support Vector Machine”.In Data Science for COVID-19, Elsevier (press), 2020

XXXIX. WHO-China Joint Mission, Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19), (2020). https://www.who.int/docs/default-source/coronaviruse/who-china-joint-mission-on-covid-19-final-report.pdf (accessed March 1, 2020)

XL. World Health Organization, Coronavirus disease 2019 (COVID-19) Situation Report – 47, (2020). https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200307-sitrep-47-covid-19.pdf?sfvrsn=27c364a4_2 (accessed March 7, 2020)

XLI. W. Wang, J. Tang, &F. Wei, “Updated understanding of the outbreak of 2019 novel coronavirus (2019‐nCoV) in Wuhan, China”. Journal of medical virology, 92(4), 441-447, 2020

XLII. Z. Y.Zu, M. D. Jiang, P. P. Xu, W. Chen, Q. Q. Ni, G. M. Lu, & L. J. Zhang, “Coronavirus disease 2019 (COVID-19): a perspective from China”. Radiology, 200490, 2020

View Download

Abstract:

In this paper, collection of research related to the effect of using nanofluids of various kinds on improving heat transfer and increasing the efficiency of solar collectors was reviewed on the other hand studies will be presented regarding the effect of electromagnetic field on improving heat transfer and its effect on solar collectors. In this paper, we have examined the electromagnetic effect of thermo-hydrodynamics behavior of nanofluid. The results of the previous research that was reviewed clearly showed that the use of nanofluids has a clear effect on improving the thermal efficiency of solar collectors and improving heat transfer in high proportions, as well as between studies that adding the effect of electromagnetic overflow on solar collector systems has had a positive effect in improving heat transfer and improving properties Physical fluid

Keywords:

Solar collector,magnetic nanofluid,Ferrofluid,Parabolic solar trough collector,Solar energy,electromagnetic field,Nanofluid,

Refference:

I. Abdulhassan A. K., Laith J. H., Ali H, A., “The Effect of Magnetic Field with Nanofluid on Heat Transfer in a Horizontal Pipe”, Al-Khwarizmi Engineering Journal.; 12,99-109, (2016).
II. Alsaady M., Fu R., Yan Y., Liu Z., Wu S., Boukhanouf R., “An Experimental Investigation on the Effect of Ferrofluids on the Efficiency of Novel Parabolic Trough Solar Collector Under Laminar Flow Conditions”, Journal Heat Transfer Engineering.; (2018).
III. Aminfar H., Mohammad P. M., Mohseni F., “Two-phase mixture model simulation of the hydro-thermal behavior of an electrical conductive ferrofluid in the presence of magnetic fields”, Journal Magn. Magn. Mater.;324, 830-842, (2012).
IV. Amir H., Hossein A. D. A.,KouroshA.,“Investigating the MHD current in presence of nanofluid inside a triangle duct in presence of electromagnetic field in form of Eulerian two phases”, Journal of Materials and Environmental Sciences.; 9, 2703-2713, (2018).
V. Ashorynejad H. R., Mohamad A. A., Sheikholeslami M., “Magnetic field effects on natural convection flow of a nanofluid in a horizontal cylindrical annulus using lattice Boltzmann method”, International Journal Thermall Science.;64, 240-250, (2013).
VI. Azizian R., Doroodchi E., McKrell T., Buongiorno J., Hu LW., Moghtaderi B., “Effect of magnetic field on laminar convective heat transfer of magnetite nanofluids”, International Journal Heat Mass Transf.; 68,94-109, (2014).
VII. Battira M., Rachid B., “Radial and Axial Magnetic Fields Effects on Natural Convection in a Nanofluid-filled Vertical Cylinder”, Journal of Applied Fluid Mechanics.; 9, 407-418, (2016).
VIII. Bradic J., Fan J., Wang W., “Penalized composite quasi-likelihood for ultrahigh-dimensional variable selection”, Journal of Royal Statistics Society.; 73, 325-349, (2011)

IX. Ellahi R., Bhatti M., Khalique C. M., “Three-dimensional flow analysis of Carreau fluid model induced by peristaltic wave in the presence of magnetic field”, Journal Mol. Liquid.; 241,1059-1068,(2017).
X. Faizal M., Saidur R., Mekhilef S., “Potential of size reduction of flat-plate solar collectors when applying MWCNT nanofluid”, 4th International Conference on Energy and Environment (ICEE), Conf.Series: Earth and Environmental Science.; 16, 012004, (2013).
XI. Gan J. G., Stanley C., Nguyen N-T., Rosengarten G., “Ferrofluids for heat transfer enhancement under an external magnetic field”, International Journal of Heat and Mass Transfer.; 123, 110-121, (2018).
XII. Ghadiri M., Sardarabadi M., Pasandideh M., Moghadam A. J., “Experimental investgation of a PVT system performance using nanoferrofluid”, Energy Conversion and Management.; 103,468-476, (2015).
XIII. Ghofrani A., Dibaei MH., Hakim S. A., Shafii MB., “Experimental investigation on laminar forced convection heat transfer of ferrofluids under an alternating magnetic field”,Expermaintal Thermal Fluid Science.; 49,193-200, (2013).
XIV. Hariri S., Mokhtari M., Gerdroodbary M. B., Fallah K., “Numerical investigation of the heat transfer of a ferrofluid inside a tube in the presence of a non-uniform magnetic field”, Eur. Phys. Journal Plus.;132,1-14, (2017).
XV. Hatami N., Banari A. K., Malekzadeh A., Pouranfard A. R., “The effect of magnetic field on nanofluids heat transfer through a uniformly heated horizontal tube”, Phys. Lett. Sect. A Gen. At. Solid State Phys.: 381, 510-515, (2017).
XVI. He Y., Wang S., Ma J., Tian F., Ren Y., “Experimental study on the light-heat conversion characteristics of nanofluids”,Nanosci. Nanotechnol Letters;. 3, 494-496, (2011).
XVII. Heidary H., Kermani M. J., DABIR B., “Magnetic Field Effect on Convective Heat Transfer Incorrugated Flow Channel” 21, 2105-2115, (2017).
XVIII. Heris S. Z., Etemad SG., Esfahan MN., “Experimental investigation of oxide nanofluids laminar flow convective heat transfer”, International Communication of Heat Mass Transfer;. 33, 529-535, (2006).

XIX. Ho C., Tsing-Tshih T., Chii-Ruey L., Hong-Ming L., Chung-Kwei L., Chih-Hung L., Hung-Ting S., “A Study of Magnetic Field Effect on Nanofluid Stability of CuO”, Materials Transactions.; 45, 1375-1378, (2004).
XX. Hussein A. K., Ashorynejad H. R., Sheikholeslami M., Sivasankaran S., “Lattice Boltzmann simulation of natural convection heat transfer in an open enclosure filled with Cu–water nanofluid in a presence of magnetic field”,Nucl. Eng. Des.;268,10-17, (2014).
XXI. Irwan N., Iskandar I. Y., Mohd R. J., “Enhancement of thermal conductivity and kinematic viscosity in magnetically controllable maghemite (c-Fe2O3) nanofluids”, Experimental Thermal and Fluid Science.; 77,265-271, (2016).

XXII. Kefayati G. R., Tang H., “Simulation of natural convection and entropy generation of MHD non-Newtonian nanofluid in a cavity using Buongiorno’s mathematical model”, International Jornal Hydrogen Energ.; 42,17284-17327, (2017).
XXIII. Khalipe V., Deshmukh P., “Experimental study of evacuated tube two phase closed thermpsyphon (TPCT) solar collector with nanofluid”, Journal of Mechanical and Civil Engineering.; 32,156-161, (2015).
XXIV. Khosravi A., Malekan M., “Effect of magnetic field on heat transfer coefficient of Fe3O4-water ferrofluid using artificial intelligence and CFD simulation”, Eur. Phys. J. Plus; (in preparation), (2018).
XXV. Khullar V., Tyagi H , Phelan P. E., OtanicarT. P. , HarjitS.,TaylorR. A., “Solar Energy Harvesting Using Nanofluids-Based Concentrating Solar Collector”, Journal of Nanotechnology in Engineering and Medicine.; 3, 031003, (2013).
XXVI. Lajvardi M., Moghimi-Rad J., Hadi I., Gavili A., Dallali I. T., Zabihi F., “Experimental investigation for enhanced ferrofluid heat transfer under magnetic field effect”, Journal MagnMagn Mater.;322,3508-13, (2010).
XXVII. Lee J-H., Lee S-H., Choi C. J., Jang S, P., Choi S. U. S., “A Review of Thermal Conductivity Data,Mechanisms and Models for Nanofluids”, nternational Journal of Micro-Nano Scale Transport.;1, 269-322, (2010).
XXVIII. Li Y., Zhou J., Tung S., Schneider E., Xi S., “A review on development of nanofluid preparation and characterization”, Powder Technology.; 196, 89-101, (2009).
XXIX. Lin T. F., Gilbert J. B., Roy G. D., “Analyses of magnetohydrodynamic propulsion with seawater for underwater vehicles”, Journal Propul Power.; 7,1081-1083, (1991).
XXX. Maouassi A., Baghidja A., Daoud S., Zeraibi N., “Numerical study of nanofluid heat transfer SiO2 through a solar flat plate collector”, International Journal Of heat and Technology.; 35, 619-625, (2017).
XXXI. Mohammad M., Ali K., Xiaowei Z., “The influence of magnetic field on heat transfer of magnetic nanofluid in a double pipe heat exchanger proposed in a small-scale CAES system”, Applied Thermal Engineering (2018).
XXXII. Mohsen S., Davood D. G., “Entropy generation of nanofluid in presence of magnetic field using Lattice Boltzmann Method”, Physica A: Statistical Mechanics and its Applications.; 417, 273-286, (2015).
XXXIII. Mohsen S., Mofid G. B., Ellahibc A. Z., “Simulation of MHD CuO–water nanofluid flow and convective heat transfer considering Lorentz forces”, Journal of Magnetism and Magnetic Materials.; 369, 69-80, (2014).
XXXIV. Mohsen S., Mofied G-B.,Ganji D-D., “Magnetic field effects on natural convection around a horizontal circular cylinder inside a square enclosure filled with nanofluid”, International Communications in Heat and Mass Transfer.; 39, 978-986, (2012).

XXXV. Mohsen S., Mohammad M. R., “Effect of space dependent magnetic field on free convection of Fe3O4–water nanofluid”, Journal of the Taiwan Institute of Chemical Engineers.;56, 6-15,(2015).
XXXVI. Mokhtari M., Hariri S., Gerdroodbary M. B., Yeganeh R., “Effect of non-uniform magnetic field on heat transfer of swirling ferrofluid flow inside tube with twisted tapes”, Chemeical Eng. Process. Process Intensif.;117,70-79, (2017).
XXXVII. Naphon P., Wiriyasart S., “Experimental study on laminar pulsating flow and heat transfer of nanofluids in micro-fins tube with magnetic fields”, International Journal Heat Mass Transfer.; 118,297-303, (2018).
XXXVIII. Omid M., Ali K., Soteris, Kalogirou A., Loan P., Somchai W., “A review of the applications of nanofluids in solar energy”, International Journal of Heat Mass Transfer.; 57, 582-594, (2013).

XXXIX. Sardarabadi M., Passandideh-Fard M., Zeinali HS., “Experimental investigation of the effects of silica/water nanofluid on PV/T (photovoltaic thermal units)”, Energy Journal.; 66,264-72, (2014).
XL. Servati A. A., Javaherdeh K., Ashorynejad H. R., “Magnetic field effects on force convection flow of a nanofluid in a channel partially filled with porous media using Lattice Boltzmann Method”,Advanc Powder Technol,;25, 666-675, (2014).
XLI. Seth G. S., Mandal P. K., “Gravity –driven convective flow of magnetite –water nanofluid and radiative heat transfer past an oscillating vertical plate in the presence of magnetic field”, Latin American Applied Research.; 48,7-13, (2018).
XLII. Sha L., Ju Y., Zhang H., Sha H. Z. L., Ju Y., “The influence of the magnetic field on the convective heat transfer characteristics of Fe3O4/water nanofluids”, Appl. Therm. Eng.; 126, 108-116, (2017).
XLIII. Sheikholeslami M., Gerdroodbary M. B., Mousavi S. V., Ganji D. D., Moradi R., “Heat transfer enhancement of ferrofluid inside an 90° elbow channel by non-uniform magnetic field”, Journal Magn. Magn. Mater.; 460, 302-311, (2018).
XLIV. SheikhzadehG A, Sebdani1 M S , Mahmoodi M , Elham S , Hashemi S E . “Effect of a Magnetic Field on Mixed Convection of a Nanofluid in a Square Cavity”, Journal of Magnetics.;18, 321-325, (2012).
XLV. Tripathi D., Bhushan S., Bg O. A., “Transverse magnetic field driven modification in unsteady peristaltic transport with electrical double layer effects”, Colloid. Surf.; 506, 32-39, (2016).
XLVI. Yang Y. T., Wang Y. H., Tseng P. K., “Numerical optimization of heat transfer enhancement in a wavy channel using nanofluids”, International Commun. Heat Mass.;55,5891-5898, (2013).
XLVII. Yousefi T., Veysi F., Shojaeizadeh E., Zinadini S., “An experimental investigation on the effect of Al2O3-H2O nanofluid on the efficiency of flat-plate solar collectors”,Renew Energy.; 39, 293-298, (2012).

View Download

Abstract:

In this article, we introduce the monomial ideals of semirings and study some of its properties. Main objective of this articleis to investigate prime spectrum of monomial ideals of semirings and discuss its topology.

Keywords:

Monomial Ideals,Prime Spectrum,Topological Semirings,Zariski Topology,

Refference:

I. A. Dochtermann and A. Engstrom, Algebraic properties of edge ideals via combinatorial topology, Electron. J. Combin., vol. 16, no. 2 pp. 16-23,2009.

II. A. I. Barvinok, Combinatorial Optimization and Computations in the Ring of Polynomials, DIMACS Technical Report, pp. 93-103,1993
III. H. Ansari-Toroghy, R. Ovlyaee-Sarmazdeh, On the prime spectrum of a module and Zariski topologies, Comm. Algebra, vol. 38, pp. 4461-4475, 2010.

IV. H.S Vandiver, Note on a simple type of algebra in which the cancellation law of addition does not hold, Bull. Amer. Math. Soc. vol. 40, No. 12, pp. 914-920, 1934.

V. J. Herzog and T. Hibi, Monomial ideals, Springer, 2011.

VI. J. S. Golan, Semirings and their applications,Kluwer Acad. Pub. Dodrecht,1999.

VII. P. J. Allen, A fundamental theorem of homomorphisms for simirings, Proc. Amer. Math. Soc., pp. 412-416, 1969.

VIII. R. Arens, J. Dugundji, Remark on the concept of compactness, Portugaliae Math., vol. 9, pp. 141-143, 1950.

IX. R. E. Atani and S. E. Atani, Ideal theory in commutative semirings, Bul. Acad. Stiinue Repub. Mold. Mat., vol. 2, pp. 14–23,2008.

X. R. E. Atani and S. E. Atani, Some remarks on partitioning semirings, An. St. Univ. Ovidius Constanta, vol. 18, pp. 49-62, 2010.

XI. R. Y. McCasland, M. E. Moore and P. F. Smith, On the spectrum of a module over a commutative ring, Comm. Algebra, vol. 25, pp. 79–103, 1997.

XII. S. Ballal and V. Kharat, Zariski topology on lattice modules, Asian-Eur. J.Math., vol. 8, no. 4 pp. 10-21, 2015

XIII. S. E. Atani, The ideal theory in quotients of commutative semirings, Glasnik Mat., vol. 42, pp. 301-308, 2007.

XIV. S. Eilenberg, Automata, languages, and machines, Academic Press, New York,vol. A, 1974.

XV. S. Hosten, G.G. Smith, Monomial ideals. InComputations in algebraic geometry with Macaulay 2, Algorithms and Computations in Mathematics, vol. 8, pp.73–100, 2011.

XVI. T.K. Mukherjee, M. K. Sen and S. Ghosh, Chain conditions on semirings, Internat. J. Math. and Math. Sci., vol. 19 no. 2, pp. 321-326, 1996.

View Download

Abstract:

The paper aims at exploring how Thai industry gives significance to supply chain performance based on supply chain strategic, tactical, and operational levels.  Together, there are 40 indicators of interest.  The questionnaire is designed and distributed to ask Thai manufacturing companies to assess the significance level of these supply chain performance indicators.  The paper explores the result based on 223 companies in Thailand who responded to the survey.  The investigations divided into two sections; (1) the identification of the most and the least significant supply chain performance of the Thai industry, and (2) the identification of the most and the least significant supply chain performance of 5 key industries in Thailand.  The discussion is then made to reflect the different concerns on each industry type.

Keywords:

Supply chain,Supply Chain Performance,Thai industry,

Refference:

I. A. Gunasekaran, C. Patel, R. E. McGaughey, “A framework for supply chain performance measurement”, International journal of production economics, Vol: 87, Issue: 3, Pages: 333-347, 2004.

II. A. Kohpaiboon, “FTAs and supply chains in the Thai automotive industry”, In ASEAN and Regional Free Trade Agreements (pp. 247-273), Routledge, 2015.

III. A. Limcharoen, V. Jangkrajarng, W. Wisittipanich, S. Ramingwong, “Thailand Logistics Trend: Logistics Performance Index”, International Journal of Applied Engineering Research, Vol: 12, Issue: 15, Pages: 4882-4885, 2017.

IV. A. Potter, P. Childerhouse, R. Banomyong, N. Supatn, N. “Developing a supply chain performance tool for SMEs in Thailand”, Supply chain management: an international journal, 2011.

V. B. M. Beamon, “Measuring supply chain performance”, International journal of operations & production management, Vol: 19,Issue: 3, Pages: 275-292, 1999.

VI. Board of Investment of Thailand, THAILAND SMART ELECTRONICS, 2017.

VII. Board of Investment of Thailand, THAILAND: THE KITCHEN OF THE WORLD, 2018.

VIII. Board of Investment of Thailand, THAILAND: WORLD’S TOP SUPPLIER OF NATURAL RUBBER, 2018.

IX. C. Shepherd, H. Günter, Measuring supply chain performance: current research and future directions. In Behavioral Operations in planning and scheduling (pp. 105-121). Springer, Berlin, Heidelberg. 2010.

X. D. G. Schniederjans, C. Curado, M. Khalajhedayati, “Supply chain digitisation trends: An integration of knowledge management”, International Journal of Production Economics, Vol: 220, Pages: 107439, 2020.

XI. G. ArzuAkyuz, T. ErmanErkan, “Supply chain performance measurement: a literature review”, International journal of production research, Vol: 48, Issue: 17, Pages: 5137-5155, 2010.

XII. Government Saving Bank, Plastic Industry of Thailand (in Thai), 2018.

XIII. H. Zsifkovits, M. Woschank, “Smart Logistics–Technologiekonzepte und Potentiale”. BHM Berg-Und HüttenmännischeMonatshefte, Vol: 164, Issue: 1, Pages: 42-45, 2019.

XIV. J. Buurman, Supply chain logistics management. McGraw-Hill, 2002.

XV. J. R. Meredith, S. M. Shafer, Operations and supply chain management for MBAs. Wiley, 2019.

XVI. J. T. Mentzer, W. DeWitt, J. S. Keebler, S. Min, N. W. Nix, C. D. Smith, Z. G. Zacharia, Z. G. “Defining supply chain management”, Journal of Business logistics, Vol: 22, Issue: 2, Pages: 1-25, 2001.

XVII. K. Schwab, The Global Competitiveness Report 2019, World Economic Forum. Geneva, 2019.

XVIII. K. Y. Tippayawong, N. Niyomyat, A. Sopadang, S. Ramingwong, “Factors affecting green supply chain operational performance of the thai auto parts industry”, Sustainability, Vol: 8, Issue: 11, Page: 1161. 2016.

XIX. Kasikorn Research Center, As neighboring countries enter global supply chains, Thai industry must safeguard its competitive edge (Current Issue No.3032), 2020.

XX. L. M. Ellram, M. L. U. Murfield, “Supply chain management in industrial marketing–Relationships matter”, Industrial Marketing Management, Vol: 79, Pages: 36-45, 2019.

XXI. M. Basheer, M. Siam, A. Awn, S. Hassan, “Exploring the role of TQM and supply chain practices for firm supply performance in the presence of information technology capabilities and supply chain technology adoption: A case of textile firms in Pakistan”. Uncertain Supply Chain Management, Vol: 7, Issue: 2, Pages: 275-288, 2019.

XXII. M. Ben-Daya, E. Hassini, Z. Bahroun, “Internet of things and supply chain management: a literature review”, International Journal of Production Research, Vol: 57, Issue: 15-16, Pages: 4719-4742, 2019.

XXIII. M. Woschank, E. Rauch, H. Zsifkovits, “A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics”. Sustainability, Vol: 12, Issue: 9, 3760, 2020.

XXIV. N. Tunpaiboon, THAILAND INDUSTRY OUTLOOK 2017-19 PHARMACEUTICALS, Krungsri Research, 2017.

XXV. Office of the National Economic and Social Development Council, THAILAND’S LOGISTICS REPORT 2018, 2019.

XXVI. P. Dallasega, M. Woschank, H. Zsifkovits, K. Tippayawong, C. A. Brown, “Requirement Analysis for the Design of Smart Logistics in SMEs”. In Industry 4.0 for SMEs (pp. 147-162). Palgrave Macmillan, Cham, 2020.

XXVII. P. Dallasega, M. Woschank, S. Ramingwong, K. Y. Tippayawong, N. Chonsawat,“Field study to identify requirements for smart logistics of European, US and Asian SMEs”, In Proceedings of the International Conference on Industrial Engineering and Operations Management, 2019.

XXVIII. R. H. Ballou, Business logistics/supply chain management: planning, organizing, and controlling the supply chain, Pearson Education India, 2007.

XXIX. R. Banomyong, N. Supatn, “Supply chain assessment tool development in Thailand: an SME perspective”, International Journal of Procurement Management, Vol: 4, Issue: 3, Pages: 244-258, 2011.

XXX. S. Boon-itt, S, “The effect of internal and external supply chain integration on product quality and innovation: evidence from Thai automotive industry”. International Journal of Integrated Supply Management, Vol: 5, Issue: 2, Page: 97, 2009.

XXXI. S. Lekuthai, “The importance of the food industry to the Thai economy: an input-output perspective”, ASEAN Economic Bulletin, Pages: 238-253, 2007.

XXXII. S. Ramingwong, S.Santiteerakul, K. Y. Tippayawong, A. Sopadang, A. Limcharoen, W. Manopiniwes, “Logistics performance of the Thai food industry”, International Journal of Advanced and Applied Sciences, Vol: 6, Issue: 5, Pages: 32-37, 2019.

XXXIII. S. Ramingwong, W. Manopiniwes, V. Jangkrajarng, “Human factors of thailand toward industry 4.0”. Management Research and Practice, Vol: 11, Issue: 1, Pages: 15-25, 2019.

XXXIV. S. Tiwong, S. Ramingwong, K.Y. Tippayawong, “On LSP Lifecycle Model to Re-design Logistics Service: Case Studies of Thai LSPs”, Sustainability, Vol: 12, Issue: 6, Pages: 2394. 2020.

XXXV. United Nations, International Standard Industrial Classification of All Economic Activities Revision 4. Department of Economic and Social Affairs Statistics Division. New York, 2008.

XXXVI. V. Jangkrajarng, A. Sopadang, K. Y. Tippayawong, W. Manopiniwes, S. Santiteerakul, S.Ramingwong, “Industrial Logistics Performance of Thai Industry”, International Journal of Engineering &Technology, Vol: 7, Issue: 3.7, Pages: 394-398, 2018.

XXXVII. W. Manopiniwes, K. Y. Tippayawong, J. Numkid, S. Santiteerakul, S. Ramingwong, P. Dallasega, “On Logistics Potential of Thai Industry in Identifying Gap to Logistics 4.0”, Journal of Engineering and Applied Sciences, Vol: 14, Issue: 5, Pages: 1608-1613, 2019.

XXXVIII. W. Yongpisanphob, THAILAND INDUSTRY OUTLOOK 2020-22: AUTOMOBILE INDUSTRY. Krungsri Research, 2019.

XXXIX. World Bank, Doing business 2019: Training for reform. Washington DC, 2019.

View Download

Abstract:

The response of single pile foundations subjected to different earthquake excitations is considered in this paper. The behavior of such foundation is important specifically in case of earthquake loading through the supporting soil medium. An axisymmetric finite element model has been implemented to simulate the behavior of pile in soil deposit using Abaqus software. Eight node axisymmetric quadrilateral elements CAX8R used to simulate the soil continuum. Contact behavior between the single pile part and the part of soil was simulated using the ‘surface to surface’ contact method with master-slave concept. Furthermore, the pile behavior material has been simulated with a linear elastic model while soil material has been simulated with an elasto-plastic model “Mohr-Coulomb failure criterion”. Three different excitation records have been adopted in the analysis: El-Centro, Halabja and Ali-Algharbi earthquake records in order to investigate the effect of various dynamic loading. The results of the analysis demonstrate alteration in the response along the pile with different soil layer with each earthquake excitation.

Keywords:

Dynamic Analysis,Single Pile,Erthquake,Abaqus Software,

Refference:

I. ABAQUS Lectures, “Analysis of Geotechnical Problems with ABAQUS”. ABAQUS, Inc., U.S.A, (2003).
II. ABAQUS/CAE User’s Manual, “Dassault Systemes Simulia Corp”. Providence, RI, USA, (2012).
III. Albusoda, B.S.; Salem, L.A.K., “The Effect Of Interaction On Pile-Raft System Settlement Subjected To Earthquake Excitation”. APPLIED RESEARCH JOURNAL, vol. 2, no. 4, pp. , (2016).
IV. Albusoda, B.S.; Salem, L.A.K., “Effect Of Pile Spacing On The Behavior Of Piled Raft Foundation Under Free Vibration And Earthquake”. Australian Journal of Basic and Applied Sciences, vol. 10, no. 12, (2016).
V. Al-Taie, A.J. and Albusoda, B.S.” Earthquake hazard on Iraqi soil: Halabjah earthquake as a case study”. Geodesy and Geodynamics, vol. 10, no.3, pp.196-204, (2019).
VI. Chenaf, N.; &Chazelas, J. L., “The Kinematic and Inertial Soil-Pile Interactions: Centrifuge Modelling”. (2008).
VII. Fattah, M.Y.; Zbar, B.S.; Mustafa, F.S. “Effect of Saturation on Response of a Single Pile Embedded in Saturated Sandy Soil to Vertical Vibration”. European Journal of Environmental and Civil Engineering, vol. 21, no. 7, pp.1-20, (2017).
VIII. Hibbit, H.D.; Karlsson, B.L, Sorrensen “ABAQUS Theory Mannul and all Manuals, Guide”. Online support, (2007).
IX. Katzenbach, R.; Schmitt, A.; Turek J. “Assessing Settlement of High-rise Structures by 3D Simulations”. Computer Aided Civil and Infrastucute Engineering, (2005).
X. Maharaja, D.K., “Load Settlement Behavior of Piled Raft Foundation by Three Dimensional Nonlinear Finite Element Analysis”. Electronic Journal of Geotechnical Engineering, (2003).
XI. Miyamoto, Y.; Fukuoka, A.; Adachi, N. and Koyamada K., “Pile response induced by internal and kinematic interaction in liquefied soil deposit (Centrifuge model test for pile foundation in saturated sand layers and its analytical study”. J. Struct. Constr. Eng., AIJ, No.494, pp.51-58, (1997).
XII. Miyamoto, Y.; Sako, Y.; Kitamura E. and Miura K., “Earthquake response of pile foundation in nonlinear liquefiable soil deposit”. J. Struct. Constr. Eng., AIJ, No.471, pp.41-50, (1995).
XIII. Miyamoto, Y.; Sako, Y.; Miura, K.; Scott, R. F. and Hushmand B., “Dynamic behavior of pile group in liquefied sand deposits”. Proc. of 10WCEE, 3, pp.1749-1754, (1992).
XIV. Thavaraj, T. Liam Finn, W.D. and Wu, G. “Seismic Response Analysis Of Pile Foundation”.  Geotechnical and Geological Engineering, vol. 28, no. 3, pp:275-286, (2010).
XV. Wriggers, P. “Finite Element Algorithms for Contact Problems”. Archives of Computational Methods in Engineering, Vol. 2, 4, 1-49, 2015.

View Download

Abstract:

The heat transfer devices involving phenomena of two phase heat transfer are proven to be the best solution for handling moderate to high heat fluxes in different applications. In this regard, an emerging and new technique is “Pulsating heat pipe cooling”, when it comes to the field of electronics thermal management. CLPHP development meets the current requirements for elimination of moving parts in a cooling system. As the demand for effective and small heat transfer devices is increasing, the present paper describes an experimental analysis of a closed loop pulsating heat pipe. Vertical bottom heat mode is considered as the position of CLPHP for the experimental work. PHP consists of a copper tube of length 262 mm, with capillary dimensions of 2 mm and 3.1 mm having internal and external diameter respectively. The tube is bent in a serpentine manner with 8 number of turns and is connected end to end. Before filling the working fluid in the tube, it is first evacuated partially. Based on the total volume, 50%, 60%, and 75 % filling ratios are considered for analysis. Different pure working fluids, viz., Ethanol, Methanol, Acetone and their mixtures, viz., Ethanol-Methanol, Ethanol-Acetone, and Methanol-Acetone are considered for experimentation. The experiments are conducted for different heat inputs varying from 20 to 100 W. The maximum heat input is dependent on the boiling point of the particular fluid. CLPHP is affected by various parameters like heat input, filling ratio, working fluid etc. Acetone shows least thermal resistance value among pure fluids whereas Ethanol-Acetone shows least thermal resistance and better heat transfer performance among mixtures. For low heat input conditions ethanol shows better performance.

Keywords:

Binary mixtures,closed loop pulsating heat pipe,fill ratio,heat input,thermal resistance,working fluids,

Refference:

I. Akachi, H. Structure of a heat pipe. US Patent No. 5219020. (1993).
II. Barua H, Ali M, Nuruzzaman M, Islam MQ, Feroz CM, 2013. Effect of filling ratio on heat transfer characteristics and performance of a closed loop pulsating heat pipe. Procedia Eng. 56:88–95.doi: 10.1016/j.proeng.2013.03.093
III. Khandekar, S. and Groll, M., 2003.”On the definition of pulsating heat pipes: An overview”,in Proceedings of the Fifth Minsk International Seminar (Heat Pipes, Heat Pumps and Refrigerators), Minsk, Belarus.
IV. Khandek;ar, S.,2004. “Thermo-hydrodynamics of closed loop pulsating heat pipes”, Ph.D., Dissertation, University of Stuttgart, Germany.
V. N. SanthiSree, NVVS Sudheer, P. Bhramara, 2019. “Experimental Analysis of Closed Loop Pulsating Heat Pipe with Different Working Fluids at Different Inclinations(2019),Journal of Jour of Adv Research in Dynamical & Control Systems, Vol. 11, No. 8.
VI. Panigrahi, P, Khandekar, S , 2010.Local hydrodynamics of flow in a pulsating heat pipe: Proceedings Frontiers in Heat Pipes (FHP), DOI: 10.5098/fhp.v1.2.3003
VII. Pramod R. Pachghare. 2016 Experimental analysis of pulsating heat pipe for air Conditioning system, International Journal of Mechanical and Production Engineering, ISSN: 2320-2092, Volume- 4, Issue-6, Jun.
VIII. Sridhara, S., Narasimha, K.R., Rajagopal, M. and Seetharamu,K.,2012 “Influence of heat input, working fluid and evacuation level on the performance of pulsating heat pipe”, Journal of Applied Fluid Mechanics, Vol. 5, No. 2, 33-42.
IX. Vipul M. Patel, H. B. Mehta 2016,“Influence of Gravity onthe Performance of Closed Loop Pulsating Heat Pipe “.Zurich Switzerland Jan 12-13, 18 (1) Part V.
X. X.M. Zhang, J.L. Xu, Z.Q. Zhou, 2004. Experimental study of a pulsating heat pipe using FC-72, ethanol, and water as working fluids, Exp. Heat Transfer 17 47–67.
XI. Yang H, Khandekar S, Groll M, 2008. Operational limit of closed loop pulsating heat pipes. Applied Thermal Eng. 28(1):49–59.doi:10.1016/j.applthermaleng.2007.01.033
XII. Yang, H.; Khandekar, S.; and Groll, M. (2009). Performance characteristics of pulsating heat pipes as integral thermal spreaders. International Journal of Thermal Science, 48 (4), 815–824.
XIII. Zhang, J.L. Xu, Z.Q. Zhou, 2004. Experimental study of a pulsating heat pipe using FC-72, ethanol, and water as working fluids, Exp. Heat Transfer 17 47–67.
XIV. Zhang, Y. and Faghri, A., 2008 “Advances and unsolved issues in pulsating heat pipes”, Heat Transfer Engineering, Vol. 29, No.1, , 20-44

View Download

Abstract:

Today, everything has been digitized, and the entire gasoline pump has a design that can display the task of controlling the pump, driving the display, quantifying the flow rate, and turning off the pump. To collect the cash, still someone is mandatory and there is a chance of many human errors. So, the main aim is to propose a system is to avoid human errors. My proposed system is petrol pump automation, which can deduct gasoline from the user card based on RFID technology without human intervention. Today, fluid supply systems are common in different places in our daily lives. Here, we will introduce the modern gasoline distribution system. To place petrol stations in remote areas is extremely precious to supply outstanding capacity to the clients. All these troubles can be solved by using this gasoline pump automation technology, which requires shorter operating time, higher efficiency and can be installed anywhere. This self-service gasoline pump device also provides customers with the protection of fueling at the gas station without any involvement of the service provider, so the risk of carrying money every time is minimized.

Keywords:

RFID,DC motor,LCD,Relay,

Refference:

I. Aishwarya Jadhav, Lajari Patil , Leena Patil , A. D. Sonawane, April 2017,“Smart Automatic Petrol Pump System“, International Journal of Science Technology and Management, vol. 6, no. 4.

II. Arabelli, R.R. &Revuri, K. 2019, “Fingerprint and Raspberri Pi based vehicle authentication and secured tracking system”, International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 5, pp. 1051-1054.

III. Kulkarni Amruta M. & Taware Sachin S., “Embedded Security System Using RFID & GSM Module”, International Journal of Computer Technology & Electronic Engineering, Volume 2 (Issue 1), Page No. 164-168.

IV. Kumar, M.A. 2019, “Security and controlling system at home by using GSM technology”, International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 9, pp. 2470-2474.

V. Nitha. C. Velayudhan, Raseena. K. R, Rashida. M. H, Risvana. M. P, Sreemol.C.V, March 2019, “Automatic Fuel Filling System”, International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), Vol. 8, Issue 3.

VI. Subba Rao, A. & Vidya Garige, S. 2019, “IoT based smart energy meter billing monitoring and controlling the loads”, International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 4S2, pp. 340-344.

VII. Vasantha, K. & Ravichander, J. 2019, “Image quality assessment for fake biometric detection: Application to iris, fingerprint, and face recognition”, International Journal of Recent Technology and Engineering, vol. 8, no. 1 Special Issue4, pp. 63-67.

VIII. Vinay Kumar, P. & Saritha, B., 2019, “Wireless arm based automatic meter reading & control system”, International Journal of Recent Technology and Engineering, vol. 7, no. 5, pp. 292-294.

IX. Wavekar Asrar A, Patel Tosif N, Pathansaddam I, Pawar H P,2016,”RFID based Automated Petrol Pump”, International Journal for Scientific Research and Development, Vol. 4, Issue 01.

View Download

Abstract:

Smartphone with the Internet is the most common item today and it provides the best online platform for businesses to trade their goods. Customers are more comfortable with online shopping and banking transactions, which are enough for hackers to cheat. Phishing attacks are now very common for smart phones. These attacks come in a variety of ways to steal customer sensitive information and payment information through fake Short Message Service(SMS) or E-Mail or Uniform Resource Locator (URL) links or applications(APPs). Therefore, the end user needs to know a few precautions to avoid phishing attackers. This paper explicitly discusses phishing attacks by their behavior and proposes a parallel defending approach to classifying messages as harm or spams using the Graphics Processing Unit (GPU) platform, which is achieved in logarithmic time of O(n log n) and also discusses the future scope.

Keywords:

Smartphone,Phishing,Mobile Security,GPU,Parallel avNNet,Smishing,

Refference:

I. Abi-Chahla, Fedy. “Nvidia’s CUDA: The End of the CPU?’.” Tom’s Hardware (2008): 1954-7.

II. Almeida, T.A., Hidalgo, J.M.G. and Yamakami, A., “Contributions to the study of sms spam filtering: New collection and results”, in Proceedings of the 11th ACM symposium on Document engineering. (2011), 259-262.

III. Amrutkar C, Kim YS, Traynor P. Detecting mobile malicious webpages in real time. IEEE Trans Mobile Comput 2017;16(8):2184–97.

IV. APWG, APWG. “Phishing Activity Trends Report: 4th Quarter 2019.” Anti-Phishing Working Group. Retrieved December 12 (2019): 2019.

V. Arachchilage, Nalin, Steve Love, and Michael Scott. “Designing a mobile game to teach conceptual knowledge of avoiding’phishing attacks’.” International Journal for e-Learning Security 2, no. 1 (2012): 127-132.

VI. Arachchilage, NalinAsankaGamagedara, and Melissa Cole. “Design a mobile game for home computer users to prevent from “phishing attacks”.” In International Conference on Information Society (i-Society 2011), pp. 485-489. IEEE, 2011.

VII. Arachchilage, NalinAsankaGamagedara, and Mumtaz Abdul Harmeed. “Integrating self-efficacy into a gamified approach to thwart phishing attacks.” arXiv preprint arXiv: 1706.07748 (2017).

VIII. Arachchilage, NalinAsankaGamagedara, and Steve Love. “A game design framework for avoiding phishing attacks.” Computers in Human Behavior 29, no. 3 (2013): 706-714.

IX. Basnet, Ram B., and TenzinDoleck. “Towards developing a tool to detect phishing URLs: a machine learning approach.” In 2015 IEEE International Conference on Computational Intelligence & Communication Technology, pp. 220-223. IEEE, 2015.

X. Besch, Matthias, and Hans Werner Pohl. “Flexible data parallel training of neural networks using MIMD-computers.” In Proceedings Euromicro

XI. CAPEC. CAPEC-164: mobile phishing; 2017. Available from https:// capec.mitre.org/data/definitions/164.html. [Accessed June 2017].

XII. Chan, P.P., Yang, C., Yeung, D.S. and Ng, W.W., “Spam filtering for short messages in adversarial environment”, Neurocomputing, Vol. 155, (2015), 167-176.

XIII. Choudhary, N. and Jain, A.K., “Towards filtering of spam messages using machine learning based technique”, in International Conference on Advanced Informatics for Computing Research, Springer. (2017), 18-30.

XIV. Cirecsan, D.; Meier, U.; Gambardella, L.M.; Schmidhuber, J. Deep big simple neural nets excel on hand-written digit recognition. arXiv: 1003.0358 v1 2010.

XV. Cormack, G.V., “Email spam filtering: A systematic review”, Foundations and Trends® in Information Retrieval, Vol. 1, No. 4, (2008), 335-455.

XVI. Cui, Qian, Guy-Vincent Jourdan, Gregor V. Bochmann, Russell Couturier, and Iosif-ViorelOnut. “Tracking phishing attacks over time.” In Proceedings of the 26th International Conference on World Wide Web, pp. 667-676. 2017.

XVII. D. Povey, A. Ghoshal, G.Boulianne, L. Burget, O.Glembek, N. Goel, M. Hannermann, P.Motl´ıˇcek, Y. Qian, P. Schwartz, J. Silovsk´y, G. Stemmer, and K. Vesel´y, “The kaldi speech recognition toolkit,” in ASRU. IEEE, 2011.

XVIII. Daniel Povey, Xiaohui Zhang, and SanjeevKhudanpur, “Parallel training of deep neural networks with natural gradient and parameter averaging,” arXiv preprint arXiv:1410.7455, 2014.

XIX. Dua, D. and Graff, C., Uci machine learning repository. 2017.

XX. El-Alfy, E.-S.M. and AlHasan, A.A., “Spam filtering framework for multimodal mobile communication based on dendritic cell algorithm”, Future Generation Computer Systems, Vol. 64, (2016), 98-107.

XXI. Fan, Chun-I., Han-Wei Hsiao, Chun-Han Chou, and Yi-Fan Tseng. “Malware detection systems based on API log data mining.” In 2015 IEEE 39th annual computer software and applications conference, vol. 3, pp. 255-260. IEEE, 2015.

XXII. Geoffrey E Hinton, Simon Osindero, and Yee-WhyeTeh, “A fast learning algorithm for deep belief nets,” Neural computation, vol. 18, no. 7, pp. 1527–1554, 2006.

XXIII. Gharvirian, F. and Bohloli, A., “Neural network based protection of software defined network controller against distributed denial of service attacks”, International Journal of Engineering, Transactions B: Applications, Vol. 30, No. 11, (2017), 1714-1722.
XXIV. Gholami, M., “Islanding detection method of distributed generation based on wavenet”, International Journal of Engineering, Transactions B: Applications, Vol. 32, No. 2, (2019), 242-248.

XXV. Goel, Diksha, and Ankit Kumar Jain. “Mobile phishing attacks and defence mechanisms: State of art and open research challenges.” Computers & Security 73 (2018): 519-544.

XXVI. Gómez Hidalgo, J.M., Bringas, G.C., Sánz, E.P. and García, F.C., “Content based sms spam filtering”, in Proceedings of the 2006 ACM symposium on Document engineering., (2006), 107-114.

XXVII. Grace, Michael, Yajin Zhou, Qiang Zhang, ShihongZou, and Xuxian Jiang. “Riskranker: scalable and accurate zero-day android malware detection.” In Proceedings of the 10th international conference on Mobile systems, applications, and services, pp. 281-294. 2012.

XXVIII. https://en.wikipedia.org/wiki/Phishing#History

XXIX. IMPERVA. Cross site scripting attacks; 2017. Available from https://www.incapsula.com/web-application-security/cross-site-scripting-xss-attacks.html. [Accessed June 2017].

XXX. Ji, H. and Zhang, H., “Analysis on the content features and their correlation of web pages for spam detection”, China Communications, Vol. 12, No. 3, (2015), 84-94.

XXXI. Junaid, M.B. and Farooq, M., “Using evolutionary learning classifiers to do mobilespam (SMS) filtering”, in Proceedings of the 13th annual conference on Genetic and evolutionary computation, (2011), 1795-1802.

XXXII. KarelVesel`y, ArnabGhoshal, Luk´asBurget, and Daniel Povey, “Sequence-discriminative training of deep neural networks,” in INTERSPEECH, 2013, pp. 2345–2349.

XXXIII. Kim, S.-E., Jo, J.-T. and Choi, S.-H., “Sms spam filterinig using keyword frequency ratio”, International Journal of Security and Its Applications, Vol. 9, No. 1, (2015), 329-336.

XXXIV. Klöckner, A. PyCuda: Even simpler GPU programming with Python. GPU Technology Conf. Proceedings, Sep. 2010, 2010.

XXXV. Klöckner, Andreas, Nicolas Pinto, Yunsup Lee, Bryan Catanzaro, Paul Ivanov, Ahmed Fasih, A. D. Sarma, D. Nanongkai, G. Pandurangan, and P. Tetali. “PyCUDA: GPU run-time code generation for high-performance computing.” Arxiv preprint arXiv 911 (2009).

XXXVI. Owens, John D., David Luebke, Naga Govindaraju, Mark Harris, Jens Krüger, Aaron E. Lefohn, and Timothy J. Purcell. “A survey of general‐purpose computation on graphics hardware.” In Computer graphics forum, vol. 26, no. 1, pp. 80-113. Oxford, UK: Blackwell Publishing Ltd, 2007.

XXXVII. ParandehMotlagh, F. and KhatibiBardsiri, A., “Detecting fake websites using swarm intelligence mechanism in human learning”, International Journal of Engineering, Transactions A: Basics, Vol. 31, No. 10, (2018), 1642-1650.

XXXVIII. Rekouche, Koceilah. “Early phishing.” arXiv preprint arXiv: 1106.4692 (2011).

XXXIX. Serrano, J.M.B., Palancar, J.H. and Cumplido, R., “The evaluation of ordered features for sms spam filtering”, in Iberoamerican Congress on Pattern Recognition, Springer., (2014), 383-390.

XL. Shahriar, Hossain, TulinKlintic, and Victor Clincy. “Mobile phishing attacks and mitigation techniques.” Journal of Information Security 6, no. 03 (2015): 206.

XLI. Sheikhi, S., M. T. Kheirabadi, and A. Bazzazi. “An Effective Model for SMS Spam Detection Using Content-based Features and Averaged Neural Network.” International Journal of Engineering 33, no. 2 (2020): 221-228.

XLII. Steinkraus, D.; Buck, I.; Simard, P. Using GPUs for machine learning algorithms. Eighth International Conference on Document Analysis and Recognition (ICDAR’05). IEEE, 2005, pp. 1115–1120.

XLIII. Su, Hang, and Haoyu Chen. “Experiments on parallel training of deep neural network using model averaging.” arXiv preprint arXiv:1507.01239 (2015).

XLIV. Sudarshan, E., and K. SeenaNaik. “A Parallel Approach for Maximum Quantization of Descendants Of Wavelet Trees.”
XLV. Suleiman, D. and Al-Naymat, G., “Sms spam detection using h2o framework”, Procedia Computer Science, Vol. 113, (2017), 154-161.

XLVI. Symantec. Symantec internet security threat report 2014, Vol. 19; 2017a.

XLVII. TaufiqNuruzzaman, M., Lee, C., Abdullah, M.F.A.b. and Choi, D., “Simple sms spam filtering on independent mobile phone”, Security and Communication Networks, Vol. 5, No. 10, (2012), 1209-1220.

XLVIII. Tewari A, Jain AK, Gupta BB. Recent survey of various defense mechanisms against phishing attacks. J Info Privacy Sec 2016;12(1):3–13.

XLIX. Uysal, A.K., Gunal, S., Ergin, S. and Gunal, E.S., “A novel framework for sms spam filtering”, in 2012 International Symposium on Innovations in Intelligent Systems and Applications, IEEE., (2012), 1-4.

L. Wardman, Brad, Michael Weideman, JakubBurgis, Nicole Harris, Blake Butler, and Nate Pratt. “A practical analysis of the rise in mobile phishing.” In Cyber Threat Intelligence, pp. 155-168. Springer, Charm, 2018.

LI. Xiaohui Zhang, Jan Trmal, Daniel Povey, and SanjeevKhudanpur, “Improving deep neural network acoustic models using generalized maxout networks,” in Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on. IEEE, 2014, pp. 215–219.

LII. Yang, Weining, AipingXiong, Jing Chen, Robert W. Proctor, and Ninghui Li. “Use of phishing training to improve security warning compliance: evidence from a field experiment.” In Proceedings of the hot topics in science of security: symposium and bootcamp, pp. 52-61. 2017.

LIII. Zainal, K., Sulaiman, N. and Jali, M., “An analysis of various algorithms for text spam classification and clustering using rapidminer and weka”, International Journal of Computer Science and Information Security, Vol. 13, No. 3, (2015), 66.

View Download

Abstract:

The authors have devised a multi-agent system for management of enormous queries by the customers in an e-commerce website. The paper discusses the phenomenon of having a first visit registration of the customers, extracting the preferences as specified by the customers, accepting the queries for products and applying Affinity Propagation Algorithm in order to obtain the clusters. These clusters are the groups of customers who share common interests in buying products offered by the e-commerce website. So, now the system has segregated the similar types of queries into distinct groups. The queries are then prioritized according to the size of the clusters, that is, the biggest cluster containing maximum number of customers has greatest priority and so on. The queries belonging to same cluster (queries with same priority) are then passed through logical intervention using Object Constraint Language to maximize resource utilization and prevent double payment.   

Keywords:

OCL,Multi Agent System,e-commerce application,customer query based cluster,Affinity Propagation Algorithm,

Refference:

I. Aßmann, U., Bartho, A., Bürger, C., Cech, S., Demuth, B., Heidenreich, F., Johannes, J., Karol, S., Polowinski, J., Reimann, J., Schroeter, J., Seifert, M., Thiele, M., Wende, C., Wilke, C.: Dropsbox: the dresden open software toolbox. Software & Systems Modeling 13(1) (2014) 133–169
II. Balsters, H.: Modelling database views with derived classes in the UML/OCL-framework. In: UML2003-The Unified Modeling Language. Modeling Languages and Applications. Springer (2003) 295–309
III. Beckert, B., Keller, U., Schmitt, P.H.: Translating the object constraint language into first-order predicate logic. In: Proceedings of VERIFY, Workshop at Federated Logic Conferences (FLoC). (2002)
IV. Case, Denise, and Scott DeLoach. “Applying an o-mase compliant process to develop a holonicmultiagent system for the evaluation of intelligent power distribution systems.” International Workshop on Engineering Multi-Agent Systems. Springer, Berlin, Heidelberg, 2013.
V. Clavel, M., Egea, M., de Dios, M.A.G.: Checking unsatisfiability for OCL constraints. In: Proceedings of the Workshop The Pragmatics of OCL and Other Textual Specification Languages. Volume 24., ECEASST (2009)
VI. DeLoach, Scott A. “O-MaSE: an extensible methodology for multi-agent systems.” Agent-Oriented Software Engineering. Springer, Berlin, Heidelberg, 2014.
VII. Demuth, B., Hussmann, H.: Using UML/OCL constraints for relational database design. In: «UML»99 – The Unified Modeling Language. Springer (1999) 598–613
VIII. Egea, M., Dania, C., Clavel, M.: MySQL4OCL: A stored procedure-based MySQL code generator for OCL. Electronic Communications of the EASST 36 (2010)
IX. Egea, M., Dania, C.: Sql-pl4ocl: an automatic code generator from ocl to sql procedural language. Software & Systems Modeling (May 2017)
X. Franconi, E., Mosca, A., Oriol, X., Rull, G., Teniente, E.: Logic foundations of the ocl modelling language. In: European Workshop on Logics in Artificial Intelligence, Springer (2014) 657–664
XI. Kant, Gaurav, et al. “Legal Semantic Web-A Recommendation System.” International Journal of Applied Information Systems (IJAIS) 7 (2014).
XII. Li, Peixin, et al. “Dynamic equivalent modeling of two-staged photovoltaic power station clusters based on dynamic affinity propagation clustering algorithm.” International Journal of Electrical Power & Energy Systems 95 (2018): 463-475.
XIII. Mandel, L., Cengarle, M.V.: On the expressive power of the object constraint language OCL. In: FM’99 — Formal Methods. Volume 1708 of Lecture Notes in Computer Science. Springer Berlin Heidelberg (1999) 854–874
XIV. Oriol, X., Teniente, E., Tort, A.: Computing repairs for constraint violations in uml/ocl conceptual schemas. Data & Knowledge Engineering 99 (2015) 39–58
XV. Queralt, A., Teniente, E.: Verification and validation of UML conceptual schemas with OCL constraints. ACM Trans. Softw. Eng. Methodol. 21(2) (2012) 13
XVI. Saadatpour, Mohsen, et al. “Priority-based Clustering in Weighted Graph Streams.” Journal of Information Science & Engineering 34.2 (2018).
XVII. Shang, Ronghua, et al. “A multiobjective evolutionary algorithm to find community structures based on affinity propagation.” Physica A: Statistical Mechanics and its Applications 453 (2016): 203-227.
XVIII. Shankhdhar, Gaurav Kant, and ManujDarbari. “Introducing Two Level Verification Model for Reduction of Uncertainty of Message Exchange in Inter Agent Communication in Organizational-Multi-Agent Systems Engineering, O-MaSE.” IOSR Journal of Computer Engineering (IOSR-JCE) IOSR Journal of Computer Engineering (IOSR-JCE) 19 (2017): 08-18.
XIX. Shankhdhar, Gaurav Kant, and ManujDarbari. “Building custom, adaptive and heterogeneous multi-agent systems for semantic information retrieval using organizational-multi-agent systems engineering, O-MaSE.” 2016 2nd International Conference on Advances in Computing, Communication, & Automation (ICACCA)(Fall). IEEE.
XX. Shankhdhar, Gaurav Kant, et al. “Fuzzy Approach to Select Most Suitable Conflict Resolution Strategy in Multi-Agent System.” 2019 International Conference on Cutting-edge Technologies in Engineering (ICon-CuTE). IEEE, 2019.
XXI. Shankhdhar, Gaurav Kant, et al. “Implementation of Validation of Requirements in Agent Development by means of Ontology” International Journal of Computer Sciences and Engineering 6 (7), 2018
XXII. Shikha Singh, ManujDarbari, “Logical Intervention in the Form of Work Breakdown Strategy using Object Constraint Language for e-Commerce Application” , International Journal of Advanced Computer Science and Applications, vol.11, No.3, pp-266-271, 2020.

XXIII. Shikha Singh, ManujDarbari, “Ontological Representation of the UML/OCL Models and Their Verifications”, International Journal of Future Generation Communication and Networking, Vol.13, No.1, pp.940-951, 2020.
XXIV. Sun, Leilei, et al. “Fast affinity propagation clustering based on incomplete similarity matrix.” Knowledge and Information Systems 51.3 (2017): 941-963.
XXV. Wei, Zexian, et al. “A novel intelligent method for bearing fault diagnosis based on affinity propagation clustering and adaptive feature selection.” Knowledge-Based Systems 116 (2017): 1-12.

View Download

Abstract:

In this article, we study the diversity in generalized reverse derivation by defining L*, R* and ( , )-*- Generalized reverse derivation in rings. We introduce some conditions which make these generalized reverse derivations and their associated *-reverse derivations to be commuting. Moreover, we discuss the conditions on these mappings that enforce the rings to be commutative

Keywords:

Reverse derivations,Prime rings,Semiprime rings,Involution,

Refference:

I. A. Aboubakr and S. Gonzalez, Generalized reverse derivations on semiprime rings, Siberian Math. J. 56(2) (2015), 199–205.

II. Filippov V. T., On δ-derivations of Lie algebras, Siberian Math. J., 39, No. 6, 1218–1230 (1998).

III. Filippov V. T., δ-Derivations of prime Lie algebras, Siberian Math. J., 40, No. 1, 174–184 (1999).

IV. Filippov V. T., On δ-derivations of prime alternative and Malcev algebras, Algebra and Logic 39 (2000), 354–358.

V. Gölbasi¨O. and Kaya K., “On Lie ideals with generalized derivations,” Siberian Math. J., 47, No. 5, 862–866 (2006).

VI. Herstein I. N., Jordan derivations of prime rings,”Proc. Amer. Math. Soc., 8, No. 6, 1104–1110 (1957).

VII. Herstein I. N., A Note on Derivations II, Canad. Math. Bull. 22 (4), (1979), 509-511.

VIII. Hopkins N. C., Generalized derivations of nonassociative algebras, Nova J. Math. Game Theory Algebra, 5, No. 3, 215–224 (1996).

IX. Kaygorodov I. B., On δ-derivations of classical Lie superalgebras, Siberian Math. J., 50, No. 3, 434–449 (2009).

X. Kaygorodov I. B., δ-Superderivations of simple finite-dimensional Jordan and Lie superalgebras, Algebra and Logic, 49, No. 2, 130–144 (2010).

XI. Kaygorodov I. B., On (n + 1)-ary derivations of simple n-aryMalcev algebras, St. Petersburg Math. J., 25, No. 4,575–585 (2014).

XII. M. Ashraf, A. Ali, S. Ali, Some commutatively theorems for rings with generalized derivations, Southeast Asian Bull. Math. 31 (2007), 415–421.

XIII. M. Ashraf, N. Rehman, On derivations and commutatively in prime rings, East-West J. Math. 3 (2001), 87–91.

XIV. M. Breˇsar, On the distance of the composition of two derivations to the generalized derivations, Glasgow Math. J. 33 (1991), 89–93.

XV. M. Breˇsar and J. Vukman, On some additive mappings in rings with involution, A equations Math. 38 (1989), 178–185.

XVI. NA. Dar, On∗-centralizing mappings in rings with involution, Georgian Math. J. 21 (2014), no. 1, 25–28.

XVII. S. Ali, B. Dhara, A. Foˇsner, Some commutatively theorems concerning additive maps and derivations on semiprime rings, in : Contemporary Ring Theory 2011, World Scientific, Hackensack (2012), 135–143.

XVIII. S.K. Tiwari, R.K. Sharma, and B. Dhara, B. Some theorems of commutatively on semiprime rings with mappings, Southeast Asian Bull.Math. 42 (2018), 579–592.

View Download

Abstract:

Electrocardiogram (ECG) plays vital role in diagnosing large number of diseases and disorders related to heart. ECG devices are able to perform multiple parameters by analyzing the patterns of bio-signals. The state-of-art ECG machine uses electrodes attached to human body using gel. The whole process agitates the patient resulting in disturbed ECG report by producing noise due to movement, imbalanced electrodes, and heavy objects. The proposed ECG system is portable finger-based system that generates ECG report in minimum time duration with providing ease to users. The system replaces disturbing electrodes by a single bio signal identification sensor. It takes signals from one finger of patient through sensor in 7 seconds. The sensor is followed up by combination of various capacitors and buffers in order to enhance signals. The signals are then transferred to software using USB port for several medical required filtrations and overall noise removal. The result of the process is an ECG signal representing heart condition of patient. The results can be stored for future medical investigations like improvement or decline in health of patient. The proposed prototype is deployed in several hospitals for testing. The system evaluated through comparison method with current system and results are satisfactory.

Keywords:

ECG,Bio-Signals,Filters,IR Sensors,Quality of Service,

Refference:

I Al-Ghamdi, Bandar. “Subcutaneous implantable cardioverter defibrillators: an overview of implantation techniques and clinical outcomes.” Current cardiology reviews 15, no. 1 (2019): 38-48.

II Betancourt, Nancy, Carlos Almeida, and Marco Flores-Calero. “T Wave Alternans Analysis in ECG Signal: A Survey of the Principal Approaches.” In International Conference on Information Technology & Systems, pp. 417-426.Springer, Cham, 2019.

III Castro, I. D., Carolina Varon, Jonathan Moeyersons, Amalia Villa Gomez, John Morales, Margot Deviaene, Tom Torfs, Sabine Van Huffel, Robert Puers, and Chris Van Hoof. “Data Quality Assessment of Capacitively-coupled ECG signals.” In Proceedings of the 2019 Computing in Cardiology Conference (CinC), Singapore, pp. 8-11. 2019.

IV Chien, Jun-Chau. “A 1.8-GHz Near-Field Dielectric Plethysmography Heart-Rate Sensor With Time-Based Edge Sampling.” IEEE Journal of Solid-State Circuits (2019).

V Dos Reis, Jesús E., Paul Soullié, Julien Oster, Ernesto PalmeroSoler, Gregory Petitmangin, Jacques Felblinger, and Freddy Odille. “Reconstruction of the 12‐lead ECG using a novel MR‐compatible ECG sensor network.” Magnetic resonance in medicine (2019).

VI El_Rahman, Sahar A. “Biometric human recognition system based on ECG.” Multimedia Tools and Applications (2019): 1-18.

VII Gao, Yang, Varun V. Soman, Jack P. Lombardi, Pravakar P. Rajbhandari, Tara P. Dhakal, Dale Wilson, Mark Poliks, KanadGhose, James N. Turner, and ZhanpengJin. “Heart Monitor Using Flexible Capacitive ECG Electrodes.” IEEE Transactions on Instrumentation and Measurement (2019).

VIII Kamp, Nicholas J., and Sana M. Al-Khatib. “The subcutaneous implantable cardioverter-defibrillator in review.” American heart journal (2019).

IX Lee, Jae-Ho, and Dong-WookSeo. “Development of ECG Monitoring System and Implantable Device with Wireless Charging.” Micromachines 10, no. 1 (2019): 38.

X Lee, Jae-Neung, Sung Bum Pan, and Keun-Chang Kwak. “Individual identification Based on Cascaded PCANet from ECG Signal.” In 2019 International Conference on Electronics, Information, and Communication (ICEIC), pp. 1-4. IEEE, 2019.

XI Majumder, Sumit, Leon Chen, OgnianMarinov, Chih-Hung Chen, Tapas Mondal, and M. Jamal Deen. “Noncontact wearable wireless ECG systems for long-term monitoring.” IEEE reviews in biomedical engineering 11 (2018): 306-321.

XII Marathe, Sachi, DilkasZeeshan, Tanya Thomas, and S. Vidhya. “A Wireless Patient Monitoring System using Integrated ECG module, Pulse Oximeter, Blood Pressure and Temperature Sensor.” In 2019 International Conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN), pp. 1-4. IEEE, 2019.

XIII Rahman, Alvee, TahsinurRahman, NawabHaiderGhani, SazzadHossain, and JiaUddin. “IoT Based Patient Monitoring System Using ECG Sensor.” In 2019 International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), pp. 378-382. IEEE, 2019.

XIV Rachim, Vega Pradana, and Wan-Young Chung. “Wearable noncontact armband for mobile ECG monitoring system.” IEEE transactions on biomedical circuits and systems 10, no. 6 (2016): 1112-1118.

XV Roopa, C. K., and B. S. Harish. “A survey on various machine learning approaches for ECG analysis.” International Journal of Computer Applications 163, no. 9 (2017): 25-33.

XVI Steinberg, Christian, François Philippon, Marina Sanchez, Pascal Fortier-Poisson, Gilles O’Hara, Franck Molin, Jean-François Sarrazin et al. “A Novel Wearable Device for Continuous Ambulatory ECG Recording: Proof of Concept and Assessment of Signal Quality.” Biosensors 9, no. 1 (2019): 17.

XVII Saadatnejad, Saeed, MohammadhoseinOveisi, and MatinHashemi. “LSTM-Based ECG Classification for Continuous Monitoring on Personal Wearable Devices.” IEEE journal of biomedical and health informatics (2019).

XVIII Wang, Ning, Jun Zhou, Guanghai Dai, Jiahui Huang, and YuxiangXie. “Energy-efficient intelligent ECG monitoring for wearable devices.” IEEE transactions on biomedical circuits and systems 13, no. 5 (2019): 1112-1121.

XIX Zhao, Peng, DekuiQuan, Wei Yu, Xinyu Yang, and Xinwen Fu. “Towards deep learning-based detection scheme with raw ECG signal for wearable telehealth systems.” In 2019 28th International Conference on Computer Communication and Networks (ICCCN), pp. 1-9.IEEE, 2019.

View Download

Abstract:

In this paper the wind energy operations in addition to its all vital issues during transients are presented. A Type III or class C type wind turbine system with induction generator is implemented which is fed from both the side of rotor and grid. As the T-III-WT-DFIG wind turbine system is effective over normal speed variation among all sustainable power sources; with variable-pitch control for variable speed it is main criteria for the motive of the research. The major issue in wind energy system design is variable speed in the power generation sectors; so this research can play an important role to define the transient analysis and fault clearances. The system is integrated with 1.5MW grid system for the analysis. Using the MATLAB Simulink, the type-III WT DFIG with variable speed wind turbine integrated with the grid system is simulated and the control action is performed by conventional PI controller in the generator and turbine coupling. In this research paper three cases such as voltage dip or sag, 3 phase fault analysis and wind speed variation are executed and the stability of the power system are discussed.

Keywords:

Type- III WT,DFIG,WECS,SVOC,wind turbine,Auto Regressive Moving Average,decoupled control,

Refference:

I. AbdulhamedHwas, Reza Katebi, Wind Turbine Control Using PI Pitch Angle Controller, IFAC Proceedings Volumes, Volume 45, Issue 3, 2012, Pages 241-246, ISSN 1474-6670, ISBN 9783902823182, https://doi.org/10.3182/20120328-3-IT-3014.00041.
II. B. P. Ganthia, S. Mohanty, P. K. Rana and P. K. Sahu, “Compensation of voltage sag using DVR with PI controller,” 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), Chennai, 2016, pp. 2138-2142, doi: 10.1109/ICEEOT.2016.7755068.
III. B. P. Ganthia, V. Agarwal, K. Rout and M. K. Pardhe, “Optimal control study in DFIG based wind energy conversion system using PI & GA,” International Conference on Power and Embedded Drive Control (ICPEDC), Chennai, 2017, pp. 343-347.
IV. Bekhada, HamaneDoumbia, Mamadou, BOUHAMIDA, Mohamed Draou, Azeddine CHAOUI, HichamBenghanem, Mustapha, “Comparative Study of PI, RST, Sliding Mode and Fuzzy Supervisory Controllers for DFIG based Wind Energy Conversion System”, International Journal of Renewable Energy Research (IJRER), Volume – 5, 2015/12/26, Page 1174 – 1185.
V. Djeriri, Youcef&Meroufel, Abdelkader&Massoum, Ahmed &Boudjema, Zinelaabidine. (2014). A comparative study between field oriented control strategy and direct power control strategy for DFIG. Journal of Electrical Engineering. 14. 169-178.
VI. IulianMunteanu, AntonetaIulianaBratcu, Nicolaos-Antonio Cutululis and Emil Ceanga, “Optimal Control of Wind Energy System”, Springer, London, 2008.
VII. Lei, Yazhou, et al. “Modeling of the wind turbine with a doubly fed induction generator for grid integration studies.” Energy Conversion, IEEE Transactions on 21.1 (2006): 257-264.
VIII. Power conversion and control of wind energy systems by Bin Wu, Yongqiang Lang, NavidZargari, Samir Kouro. IEEE publication.
IX. Qiao, Wei. “Dynamic modeling and control of doubly fed induction generators driven by wind turbines.” Power Systems Conference and Exposition, 2009. PSCE’09. IEEE/PES. IEEE, 2009.
X. S. M. Muyeen, Md. Hasan Ali, R. Takahashi, T. Murata, J. Tamura, Y. Tomaki, A. Sakahara and E. Sasano, “Comparative Study on Transient Stability Analysis of Wind Turbine Generator System Using Different Drive Train Models”, IET Renewable Power Generation, Vol. 1, No, 2, pp. 131-141, June 2007.
XI. Siraj, Kiran, HarisSiraj, and MashoodNasir. “Modeling and control of a doubly fed induction generator for grid integrated wind turbine.” Power Electronics and Motion Control Conference and Exposition (PEMC), 2014 16th International. IEEE, 2014.
XII. T.ghennam, E.M. Berkouk, B. Francois, “Modeling and Control of a Doubly Fed Induction Generator (DFIG) Based Wind Conversion System” IEEE 2009.
XIII. Tao Sun, “Power Quality of Grid-Connected Wind Turbines with DFIG and Their Interaction with the Grid”, Ph.D. dissertation, Aalborg University, Denmark, May 2004.
XIV. Yang, Jin. “Fault analysis and protection for wind power generation systems”. Diss. University of Glasgow, 2011.
XV. Zhang, B.; Hu, W.; Hou, P.; Tan, J.; Soltani, M.; Chen, Z. “Review of Reactive Power Dispatch Strategies for Loss Minimization in a DFIG-based Wind Farm” Energies2017, 10, 856.

View Download

Abstract:

Reconnaissance or footprinting is the technique used for gathering information about computer systems and the entities they belong to. To exploit any system, a hacker might use various tools and technologies. This information is very useful to a hacker who is trying to crack a whole system. Subdomain enumeration plays a vital role in reconnaissance. Enumeration of subdomains provide an important insight towards the various underlying architecture and enable to find hidden user interfaces and admin panels. The less infrequent and unknown the domain name, the less visitors will visit the site. This enables a blindspot for the easy finding of low hanging vulnerabilities. Some of the most popular various tools used for recon on domains are Amass, Subfinder, KnockPy, altdns, sublis3r. We have done a comparative study and analysis of various functions of these tools on parameters like uniqueness, accuracy, complexity and conclude which works in certain scenarios along with static code analysis to find weak spots within the code infrastructure of each of the tools.

Keywords:

reconnaissance,web security,application security,

Refference:

I. A. Kothia, B. Swar and F. Jaafar, “Knowledge Extraction and Integration for Information Gathering in Penetration Testing,” 2019 IEEE 19th International Conference on Software Quality, Reliability and Security Companion (QRS-C), Sofia, Bulgaria, 2019, pp. 330-335.
II. Adiwal, Sanjay &Rajendran, Balaji&Shetty, Pushparaj.(2018). Domain Name System (DNS) Security: Attacks Identification and Protection Methods.
III. AlkaAgrawal, MamdouhAlenezi, Rajeev Kumar and Raees Ahmad Khan, Securing Web Applications through a Framework of Source Code Analysis, Journal of Computer Science,Volume 15, Issue 12,Pages 1780-1794
IV. https://github.com/aboul3la/Sublist3r
V. https://github.com/guelfoweb/knock
VI. https://gitlab.com/paperrepo/subdomain-enumeratioon
VII. https://github.com/infosec-au/altdns
VIII. https://github.com/OWASP/Amass
IX. https://github.com/projectdiscovery/subfinder
X. K. Nirmal, B. Janet And R. Kumar, “Web Application Vulnerabilities- The Hacker’s Treasure,” 2018 International Conference On Inventive Research In Computing Applications (Icirca), Coimbatore, India, 2018, Pp. 58-62
XI. P. Harika Reddy SurapaneniGopi Siva SaiTeja,Cyber Security and Ethical Hacking,International Journal for Research in Applied Science & Engineering Technology (IJRASET),Volume 6 Issue VI, June 2018
XII. Richard Roberts and Dave Levin. 2019. When Certificate Transparency Is Too Transparent: Analyzing Information Leakage in HTTPS Domain Names. In Proceedings of the 18th ACM Workshop on Privacy in the Electronic Society (WPES’19).Association for Computing Machinery, New York, NY, USA, 87–92.
XIII. Russell, Rebecca & Kim, Louis & Hamilton, Lei &Lazovich, Tomo&Harer, Jacob &Ozdemir, Onur&Ellingwood, Paul &McConley, Marc. (2018). Automated Vulnerability Detection in Source Code Using Deep Representation Learning. 757-762. 10.1109/ICMLA.2018.00120.
XIV. S. M. Zia Ur Rashid ImtiazKamrulImtiazKamrulAsrafulAlamAsrafulAlam,Understanding the Security Threats of Esoteric Subdomain Takeover and Prevention Scheme, Conference: 2019 International Conference on Electrical,doi: 10.1109/ECACE.2019.8679122
XV. Siavvas M., Gelenbe E., Kehagias D., Tzovaras D. (2018) Static Analysis-Based Approaches for Secure Software Development. In: Gelenbe E. et al. (eds) Security in Computer and Information Sciences. Euro-CYBERSEC 2018.Communications in Computer and Information Science, vol 821. Springer, Cham
XVI. Thomassen, P., Benninger, J., &Margraf, M. (2018).Hijacking DNS Subdomains via Subzone Registration: A Case for Signed Zones. OJWT, 5, 6-13.

View Download

Abstract:

Any sort of security compromise of the system will directly impact individual lifestyle. Therefore security and privacy of this particular technology is foremost vital concern to fix. Within this paper our experts present a thorough research of security issues in IoT and also classify achievable cyber- attacks on each coating of IoT construction. Our company like wise goes over problems to standard security options including cryptographic services, verification mechanisms and also essential management in IoT.

Keywords:

IoT,network security,challenges,

Refference:

I. Ashton, K. “That ‘Web of Qualities’ factor”. Quickly available online: http://www.rfidjournal.com/ (accessed on 22 June 2009).
II. A. J. Menezes, S. A. Vanstone, P. C. Van Oorschot, “Handbook of Applied Cryptography”, CRC Push, Inc., Boca Raton, FL, 1996.
III. A. Monelli and S. B. Sriramoju, “An Overview of the Challenges and Applications towards Web Mining,” 2018 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 2018 2nd International Conference on, Palladam, India, 2018, pp. 127-131.doi: 10.1109/I-SMAC.2018.8653669
IV. D. Slonim, P. Tamayo, J. Mesirov, T. Golub, and E. Lander. Class prediction and discovery using gene expression data. In Proc. 4th Int. Conf. on Computational Molecular Biology (RECOMB), 2000, pages263–272.
V. D. Deepika, a Krishna Kumar, MonelliAyyavaraiah, ShobanBabuSriramoju, “Phases of Developing Artificial Intelligence and Proposed Conversational Agent Architecture”, International Journal of Innovative Technology and Exploring Engineering (IJITEE), ISSN: 2278-3075, Volume-8 Issue-12, October 2019, DOI: 10.35940/ijitee.L3384.1081219
VI. Kiran Kumar S V N Madupu, “Key Methodologies for Designing Big Data Mining Platform Based on CloudComputing”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 1 Issue 2, pp. 190-196, September-October 2016. Available at doi : https://doi.org/10.32628/CSEIT206271
VII. Kiran Kumar S V N Madupu, “Opportunities and Challenges Towards Data Mining with Big Data”, International Journal of Scientific Research in Science and Technology (IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 1 Issue 3, pp. 207-214, July-August 2015. Available at doi : https://doi.org/10.32628/IJSRST207255
VIII. Kiran Kumar S V N Madupu, “A Survey on Cloud Computing Service Models and Big Data Driven Networking”, International Journal of Scientific Research in Science and Technology (IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 4 Issue 10, pp. 451-458, September-October 2018. Available at doi : https://doi.org/10.32628/IJSRST207257
IX. Laurent, (2014) “Lighting in weight collective critical establishment system for the Net of Qualities” Pc Networks, vol. 64, pp. 273– 295.
X. P. Pramod Kumar, S. Naresh Kumar, V. Thirupathi, Ch. Sandeep, “QOS AND SECURITY PROBLEMS IN 4G NETWORKS AND QOS MECHANISMS OFFERED BY 4G”, International Journal of Advanced Science and Technology, Vol. 28, No. 20, (2019), pp. 600-606
XI. Pasha, S.N., Ramesh, D., Kodhandaraman, D. &Salauddin, M.D. 2019, “An research to enhance the old manuscript resolution using deep learning mechanism”, International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 6 Special Issue 4, pp. 1597-1599.

XII. P. Pramod Kumar, C. H. Sandeep, and S. Naresh Kumar.”An overview of the factors affecting handovers and effective highlights of handover techniques for next generation wireless networks.” Indian Journal of Public Health Research & Development, no. 11 (2018): 722-725.
XIII. Pushpa Mannava, “A Study on the Challenges and Types of Big Data”, “International Journal of Innovative Research in Science, Engineering and Technology”, ISSN(Online) : 2319-8753, Vol. 2, Issue 8, August 2013
XIV. Pushpa Mannava, “Data Mining Challenges with Bigdata for Global pulse development”, International Journal of Innovative Research in Computer and Communication Engineering, ISSN(Online): 2320-9801, vol 5, issue 6, june 2017
XV. Pushpa Mannava, “Big Data Analytics in Intra-Data Center Networks and Components Of Data Mining”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 1 Issue 3, pp. 82-89, November-December 2016. Available at doi : https://doi.org/10.32628/CSEIT206272
XVI. Sandeep, C. H., S. Naresh Kumar, and P. Pramod Kumar.”Security challenges and issues of the IoT system.” Indian Journal of Public Health Research & Development, no. 11 (2018): 748-753.
XVII. Sheshikala, M et al, “Natural Language Processing and Machine Learning Classifier used for Detecting the Author of the Sentence ”. International Journal of Recent Technology and Engineering (IJRTE) (2019).
XVIII. S. Naresh Kumar, P. Pramod Kumar, C. H. Sandeep, and S. Shwetha. “Opportunities for applying deep learning networks to tumour classification.” Indian Journal of Public Health Research & Development, no. 11 (2018): 742-747.
XIX. Sripada, Naresh Kumar et al. “Support Vector Machines to Identify Information towards Fixed-Dimensional Vector Space.”International Journal of Innovative Technology and Exploring Engineering (IJITEE),(2019).

View Download

Abstract:

The early medical diagnosis and also outlook of a cancer kind have actually ended up being a requirement in cancer investigation, as it can assist in the succeeding scientific control of people. The usefulness of categorizing cancer clients right into higher or reduced risk groups has led lots of re- hunt staffs, coming from the biomedical as well as the bioinformatics field, to study the application of machine learning (ML) approaches. For that reason, these strategies have been actually taken advantage of as a goal to model the advancement and also treatment of malignant disorders. Additionally, the capability of ML devices to discover key attributes from complex datasets shows their value.

Keywords:

Machine Learning,Deep learning,

Refference:

I A. Monelli and S. B. Sriramoju, “An Overview of the Challenges and Applications towards Web Mining,” 2018 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 2018 2nd International Conference on, Palladam, India, 2018, pp. 127-131.doi: 10.1109/I-SMAC.2018.8653669
II D. Slonim, P. Tamayo, J. Mesirov, T. Golub, and E. Lander. Class prediction and discovery using gene expression data. In Proc. 4th Int. Conf. on Computational Molecular Biology (RECOMB), 2000, pages263–272.
III D. Deepika, a Krishna Kumar, MonelliAyyavaraiah, ShobanBabuSriramoju, “Phases of Developing Artificial Intelligence and Proposed Conversational Agent Architecture”, International Journal of Innovative Technology and Exploring Engineering (IJITEE), ISSN: 2278-3075, Volume-8 Issue-12, October 2019, DOI: 10.35940/ijitee.L3384.1081219
IV Golub, Todd R., et al. “Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.” science 286.5439 (1999):531-537.
V Kiran Kumar S V N Madupu, “Tool to IntegrateOptimized Hardware and Extensive Software into Its Database to Endure Big Data Challenges”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 5, pp. 272-279, September-October 2019. Available at doi : https://doi.org/10.32628/CSEIT206275
VI Kiran Kumar S V N Madupu, “Key Methodologies for Designing Big Data Mining Platform Based on CloudComputing”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 1 Issue 2, pp. 190-196, September-October 2016. Available at doi : https://doi.org/10.32628/CSEIT206271
VII Kiran Kumar S V N Madupu, “Opportunities and Challenges Towards Data Mining with Big Data”, International Journal of Scientific Research in Science and Technology (IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 1 Issue 3, pp. 207-214, July-August 2015. Available at doi : https://doi.org/10.32628/IJSRST207255
VIII Komuravelly Sudheer Kumar et al, “A Narrative Improvement Techniques Used with The Expert Systems.” (2019).
IX Lakhani, Sunil R., and Alan Ashworth. “Microarray and histopathological analysis of tumours: the future and the past?.” Nature Reviews Cancer 1.2 (2001):151-157.
X Nguyen, Danh V., and David M. Rocke. “Classification of acute leukemia based on DNA microarray gene expressions using partial least squares.” Methods of Microarray Data Analysis. Springer US, 2002.109-124.
XI Pasha, S.N., Ramesh, D., Kodhandaraman, D. &Salauddin, M.D. 2019, “An research to enhance the old manuscript resolution using deep learning mechanism”, International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 6 Special Issue 4, pp. 1597-1599.
XII P. Pramod Kumar, C. H. Sandeep, and S. Naresh Kumar.”An overview of the factors affecting handovers and effective highlights of handover techniques for next generation wireless networks.” Indian Journal of Public Health Research & Development, no. 11 (2018): 722-725.
XIII Pushpa Mannava, “Big Data Analytics in Intra-Data Center Networks and Components Of Data Mining”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 1 Issue 3, pp. 82-89, November-December 2016. Available at doi : https://doi.org/10.32628/CSEIT206272

XIV Pushpa Mannava, “An Overview of Cloud Computing and Deployment of Big Data Analytics in the Cloud”, International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Online ISSN : 2394-4099, Print ISSN : 2395-1990, Volume 1 Issue 1, pp. 209-215, 2014. Available at doi : https://doi.org/10.32628/IJSRSET207278
XV Pushpa Mannava, “Role of Big Data Analytics in Cellular Network Design”, International Journal of Scientific Research in Science and Technology (IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 1 Issue 1, pp. 110-116, March-April 2015. Available at doi : https://doi.org/10.32628/IJSRST207254
XVI Sandeep, C. H., S. Naresh Kumar, and P. Pramod Kumar.”Security challenges and issues of the IoT system.” Indian Journal of Public Health Research & Development, no. 11 (2018): 748-753.
XVII Sheshikala, M et al, “Natural Language Processing and Machine Learning Classifier used for Detecting the Author of the Sentence”. International Journal of Recent Technology and Engineering (IJRTE) (2019).
XVIII S. Naresh Kumar, P. Pramod Kumar, C. H. Sandeep, and S. Shwetha. “Opportunities for applying deep learning networks to tumour classification.” Indian Journal of Public Health Research & Development, no. 11 (2018): 742-747.
XIX Siripuri Kiran, Shoban Babu Sriramoju, “A Study on the Applications of IOT”, Indian Journal of Public Health Research & Development, November 2018, Vol.9, No. 11, DOI Number: 10.5958/0976-5506.2018.01616.9
XX Sripada, Naresh Kumar et al. “Support Vector Machines to Identify Information towards Fixed-Dimensional Vector Space.”International Journal of Innovative Technology and Exploring Engineering (IJITEE),(2019).
XXI J Manasa, SN Kumar.”Distinguishing Stress Based on Social Interactions in Social Content Area”.International Journal of Pure and Applied Mathematics, 2018
XXII Sheshikala, M., Kothandaraman, D., VijayaPrakash, R. &Roopa, G. 2019, “Natural language processing and machine learning classifier used for detecting the author of the sentence”, International Journal of Recent Technology and Engineering, vol. 8, no. 3, pp. 936-939.

View Download