Archive

Abstract:

The concept of intuitionistic fuzzy d-filter of d-algebra introducing in this paper and also several properties are discussing, with studding some relations on this notation with the concept of intuitionistic fuzzy d-algebra.

Keywords:

d-algebra,filter,d-filter,intuitionistic fuzzy set,fuzzy set,

Refference:

I. A. K. Hassan, “fuzzy filter spectrum of d-algebra”, M.Sc. Thesis, Faculty of Education for Girls, University of Kufa. (2014)
II. D. Coker, “An introduction to intuitionistic fuzzy topological spaces”, Fuzzy Sets and Systems 88 (1997), 81–89.
III. J. Neggers, A. Dvurecenskij and H. S. Kim, “On d-fuzzy Function in d-algebras” foundations of physics, 30(2000), No. 10, 1807-1816.
IV. J. Neggers and H. S. Kim, “on d-algebra “, Math. Slovaca. 49(1999) No.1, 19-26.
V. K. Iseki, “An algebra Relation with Propositional Calculus” Proc. Japan Acad, 42 (1966) 26-29.
VI. K. T. Atanassov, “Intuitionistic fuzzy sets” , Fuzzy sets and Systems 35 (1986), 87–96.
VII. L. A. Zadeh, “Fuzzy set”,Inform. And Control. 8(1965), 338-353.
VIII. P. A. Ejegwa, S.O. Akowe, P.M. Otene, J.M. Ikyule,”An Overview On Intuitionistic Fuzzy Sets ” International Journal of scientific & technology research , 3(2014) , 3, 2277-8616
IX. P. J. Allen, H. S. Kim, and J. Neggers, “Companion d-algebra” , Math. Slovaca 57(2007), No. 2 , 93-106
X. Y. B. Jun, H. S. Kim and D.S. Yoo, “Intuitionistic fuzzy d-algebra”, Scientiae Mathematicae Japonicae Online, e-(2006), 1289–1297.
XI. Y. Iami and K. Iseki, “On Axiom System of Propositional Calculi XIV” Proc. Japan Acad, 42 (1966) 19-20.

A NOVEL APPROACH FOR EASY CHITS USING AN ANDROID APPLICATION

Authors:

P. Praveen, Ch. Sai Krishna, M. Hrushikesh, G. Sai Kumar, B. Pranay Kumar

DOI NO:

https://doi.org/10.26782/jmcms.2020.06.00028

Abstract:

The aim of this project is to develop an android application software package “EASY CHITS” for small scale chit organizers who could not afford chit fund software. This is an end-to-end application which covers almost all the activities involved in managing a chit, everything in this application is systematically organized and arranged for both the chit organizers and users, unlike other applications each and every activity is arranged in three modules namely total balance, chit details, history , which makes it simple to use and navigate through the entire application for chit organizers, In addition to that all the necessary information is included for users at the user end.Chit Funds are indigenous monetary establishments in India that consolidates credit and investment funds in a solitary plan. In a chit support plot, a gathering of people meet up for a foreordained timespan and add to a typical pool at customary interims. The quantity of chit plans enlisted has been diminishing throughout the years. The chit support individuals show that as much as 72 percent of the individuals take an interest in chit assets for sparing. Moreover, 96 percent of the current and non-current chit finance individuals feel that chit reserves are sheltered. Larger part of the current and non-current chit support individuals have a place with low-salary family units. Our discoveries point to the way that however chit reserves are a significant wellspring of money for independent companies and low-pay family units in India; there has been a general mass migration of low worth chit plans from the enrolled chit support showcase. This is for the most part in light of the fact that enlisted chit subsidizes think that its less worthwhile to serve the poor because of the expanded expense of working such plans forced by the controllers. We find that the chit finance industry tends to the reserve funds needs of individuals, is viewed as sheltered and furthermore offers credits at lower loan costs than moneylenders.

Keywords:

Classification,Cluster,Easy chits,Android,UPI,

Refference:

I. http://business.mapsofindia.com/investment-industry/chit-funds.html

II. https://faculty.iima.ac.in/~iffm/literacy/Chit-fund-field-survey-report.pdf

III. https://ijrar.com/upload_issue/ijrar_issue_1459.pdf

IV. https://journals.sagepub.com/doi/abs/10.1177/097492921100300305.

V. http://shreyaschits.com/faq_aboutchits.html

VI. Mohammed Ali Shaik, P. Praveen, Dr. R. Vijaya Prakash, “Novel Classification Scheme for Multi Agents”, Asian Journal of Computer Science and Technology, ISSN: 2249-0701 Vol.8 No.S3, 2019, pp. 54-58.

VII. P. Praveen, B. Rama and T. Sampath Kumar, “An efficient clustering algorithm of minimum Spanning Tree,” 2017 Third International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB), Chennai, 2017, pp. 131-135.doi: 10.1109/AEEICB.2017.7972398

VIII. P. Praveen, B. Rama, “An Efficient Smart Search Using R Tree on Spatial Data”,Journal of Advanced Research in Dynamical and Control Systems, Issue 4,ISSN:1943-023x.

IX. Praveen P., Rama B. (2018) A Novel Approach to Improve the Performance of Divisive Clustering-BST. In: Satapathy S., Bhateja V., Raju K., Janakiramaiah B. (eds) Data Engineering and Intelligent Computing. Advances in Intelligent Systems and Computing, vol 542. Springer, Singapore.

X. Praveen P., Rama B(2020). “An Optimized Clustering Method To Create Clusters Efficiently” Journal Of Mechanics Of Continua And Mathematical Sciences , ISSN (Online) : 2454 -7190 Vol.-15, No.-1, January (2020) pp 339-348 ISSN (Print) 0973-8975,https://doi.org/10.26782/jmcms.2020.01.00027

XI. Sallauddin Mohmmad, Dr. M. Sheshikala, Shabana,” Software Defined Security (SDSec):Reliable centralized security system to decentralized applications in SDN and their challenges”, Jour of Adv Research in Dynamical & Control Systems, Vol. 10, 10-Special Issue, 2018, pp. (147-152).

XII. M. Sheshikala, D. Rajeswara Rao and R. Vijaya Prakash, Computation Analysis for Finding Co– Location Patterns using Map–Reduce Framework, Indian Journal of Science and Technology, Vol 10(8), DOI: 10.17485/ijst/2017/v10i8/106709, February 2017.

XIII. https://www.drishtiias.com/to-the-points/paper3/chit-fund

XIV. https://www.dvara.com/wp-content/uploads/2011/03/REPORT-Chit-Funds-Innovative-Access-to-Finance.pdf

XV. http://www.mca.gov.in/Ministry/pdf/Chit_Fund_Companies_6nov2008.pdf

XVI. http://www.telegraphindia.com/1130428/jsp/7days/story_16836319.jsp#.U4ghv_mSwoo

A SURVEY PAPER ON CONVOLUTION NEURAL NETWORK IN IDENTIFYING THE DISEASE OF A COTTON PLANT

Authors:

M. Sheshikala, D. Ramesh, P. Kumara Swamy, R. Vijaya Prakash

DOI NO:

https://doi.org/10.26782/jmcms.2020.06.00029

Abstract:

One of the significant areas of Indian Economy is Agriculture. Work to practically half of the nation’s workforce is given by Indian horticulture segment. As a part of Agriculture, Cotton plays a major role in economic resource of Telangana. Huge number of farmers grows cotton in their fields as the lands fit to that crop. Beside the advantage the major problem affecting the crop are the diseases that are unknown to the farmers at early stages and losing the entire crop when he gets aware on that. As a solution, we can identify the disease in the early stage and rectify before it affects the entire crop. This can be done by looking into images collected from the crop and given it as a test sample to the convolution neural network, where we test the sample with the existing training data and identify the major areas that are affected with the disease. As an improvement we can also identify the disease that is also affected and apply the required pesticides. As a result, 91% of the diseases were correctly identified.

Keywords:

Neural Networks,Layers,Filter,Pooling,Padding,softmax,

Refference:

I. Aakanksha Rastogi, Ritika Arora, Shanu Sharma, “Leaf Disease Detection and Grading using Computer Vision Technology &Fuzzy Logic,” presented at the 2nd International Conference on Signal Processing and Integrated Networks (SPIN), IEEE, 2015, pp. 500–505.
II. A. Harshavardhan, S. Babu and T. Venugopal, “An Improved Brain Tumor Segmentation Method from MRI Brain Images,” 2017 2nd International Conference On Emerging Computation and Information Technologies (ICECIT), Tumakuru, 2017, pp. 1-7.
III. Barbedo, J.G., 2018. Factors influencing the use of deep learning for plant disease recognition. Biosystems engineering, 172, pp.84-91.
IV. Harshavardhan, A. Mohammad, M.D. S. Ramesh, D. RaviChythanya, K,” Design methods for detecting sensor node failure and node scheduling scheme for WSN”, International Journal of Engineering and Advanced Technology 9 (1) ,pp.5430, 2019
V. Khirade, Sachin D. and A. B. Patil. “Plant disease detection using image processing.” In 2015 International conference on computing communication control and automation, pp. 768-771. IEEE, 2015.
VI. Liu, Bin, Yun Zhang, DongJian He, and Yuxiang Li. “Identification of apple leaf diseases based on deep convolutional neural networks.” Symmetry 10, no. 1 (2018): 11
VII. Md. Nazrul Islam, M.A. Kashem, MahmudaAkter and Md. Jamilur Rahman, “An Approach to Evaluate Classifiers for Automatic Disease Detection and Classification of Plant Leaf,” presented at the International Conference on Electrical, Computer and Telecommunication Engineering, RUET, Rajshahi-6204, Bangladesh, 2012, pp. 626–629.
VIII. Prakash, RajanalaVijaya, and SrinathTaduri. “Safe Navigation for Elderly and Visually Impaired People Using Adhesive Tactile Walking Surface Indicators in Home Environment.” In Information and Communication Technology for Sustainable Development, pp. 771-778. Springer, Singapore, 2020.
IX. Roopa, Goje, and M. Sampath Reddy. “A study on pattern matching intrusion detection system for providing network security to improve the overall performance of security system.” Indian Journal of Public Health Research & Development 9, no. 11 (2018): 683-687.
X. Sallauddin, M. Ramesh, D. Harshavardhan, A. Pasha, S.N. Shabana, “A comprehensive study on traditional AI and ANN architecture”, International Journal of Advanced Science and Technology 28 (17) ,pp.479, 2019
XI. Shaik, Mohammed Ali, P. Praveen, and R. VijayaPrakash. “Novel Classification Scheme for Multi Agents.”, Asian Journal of Computer Science and Technology 8, no. S3 (2019): 54-58.
XII. Traore, B.B. Kamsu-Foguem, B. and Tangara, F., 2018. Deep convolution neural network for image recognition. Ecological informatics, 48, pp.257-268.
XIII. Praveen P., Rama B(2020). “An Optimized Clustering Method To Create Clusters Efficiently” Journal Of Mechanics Of Continua And Mathematical Sciences , ISSN (Online) : 2454 -7190 Vol.-15, No.-1, January (2020) pp 339-348 ISSN (Print) 0973-8975, https://doi.org/10.26782/jmcms.2020.01.00027

EFFECT OF IGNITION TIMINGS ON THE SI ENGINE PERFORMANCE AND EMISSIONS FUELED WITH GASOLINE, ETHANOL AND LPG

Authors:

Mohanad Aldhaidhawi, Muneer Naji, Abdel Nasser Ahmed

DOI NO:

https://doi.org/10.26782/jmcms.2020.06.00030

Abstract:

The engine performance, combustion characteristics and exhaust gas emissions of a four-cylinder, four-stroke indirect injection spark ignition engine has been numerically investigated at constant engine speed and different ignition timings when using gasoline, ethanol and LPG fuels. For this purpose, a model has been suggested by using a two-zone burnt and unburnt gas for in-cylinder combustion. The experimental data related to the cylinder pressures have been carried out to validate the engine model. The optimal effective power and effective torque were shown at advanced crank angle degrees before the top dead center. It is observed that the brake specific fuel consumption decreases if the ignition timings increase. The ethanol fuel exhausted a minimum level of carbon monoxide, unburnt hydrocarbon and oxide nitrogen emissions when compared with the gasoline fuel at all operating conditions. LPG fuel produced promising good emission results than that obtains from gasoline fuel.

Keywords:

LPG and Ethanol fuels,SI engine,Engine performance,Emissions,

Refference:

I. B. Erkuş, A. Sürmen, M. İ. Karamangil, “A comparative study of carburation and injection fuel supply methods in an LPG-fuelled SI engine,” Fuel, vol. 107, pp. 511–517, May 2013.

II. C. D. Rakopoulos, C. N. Michos, E. G. Giakoumis. “Availability analysis of a syngas fueled spark ignition engine using a multi-zone combustion model,” Energy, vol. 33, no. 9, pp. 1378-1398, September 2008.

III. C. Ji, C. Liang, S. Wang, “Investigation on combustion and emissions of DME/gasoline mixtures in bja spark-ignition engine,” Fuel, vol. 90, no. 3, pp. 1133-1138, Mar. 2011.

IV. C. Park, S. Oh, T. Kim, H. Oh, C. Bae, “Combustion Characteristics of Stratified Mixture in Lean-Burn Liquefied Petroleum Gas Direct-Injection Engine with Spray-Guided Combustion System,” Journal of Engineering for Gas Turbines and Power, vol. 138, no. 7, PP. 071501, Jul 2016.

V. C. P. Cooney, J. J. Worm, J. D. Naber, “Combustion characterization in an internal combustion engine with ethanol-gasoline blended fuels varying compression ratios and ignition timing,” Energy & Fuels, vol. 23, no. 5, pp. 2319-2324, April 2009.

VI. E. Hu, Z. Huang, B. Liu, J. Zheng, X. Gu, “Experimental study on combustion characteristics of a spark-ignition engine fueled with natural gas–hydrogen blends combining with EGR,” International journal of hydrogen energy, vol. 34, no. 2, pp. 103 5-1044, January 2009.

VII. E. Singh, K. Morganti, R. Dibble, “Dual-fuel operation of gasoline and natural gas in a turbocharged engine,” Fuel, vol. 237, pp. 694-706, February 2019.

VIII. H. Bayraktar O. Durgun, “Investigating the effects of LPG on spark ignition engine combustion and performance,” Energy Conversion and Management, vol. 46, no. 14, pp. 2317-2333, August 2005.

IX. H. Bayraktar, “An experimental study on the performance parameters of an experimental CI engine fueled with diesel–methanol–dodecanol blends,” Fuel, vol. 87, no. 2, pp. 158–164, February 2008.

X. H. Hedfi, A. Jbara, H. Jedli, K. Slime, A. Stoppato, “Performance enhancement of a spark ignition engine fed by different fuel types Performance enhancement of a spark ignition engine fed by different fuel types,” Energy Conversion and Management, vol. 112, pp. 166–175, Mar. 2016.

XI. K. Dheeraj, B. Veeresh, K. Vijay, “Effects of LPG on the performance and emission characteristics of SI engine – An Overview,” IJEDR, vol. 2, no. 3, pp. 2997-3003, 2014.

XII. K. Kim, J. Kim, S. Oh, C. Kim, Y. Lee, “Lower particulate matter emissions with a stoichiometric LPG direct injection engine,” Fuel, vol. 187, no. 1, pp. 197–210, January 2017.

XIII. K. Kim, J. Kim, S. Oh, C. Kim, Y. Lee, “Evaluation of injection and ignition schemes for the ultra-lean combustion direct-injection LPG engine to control particulate emissions,” Applied Energy, vol. 194, pp. 123-135, May 2017.

XIV. L. Tunka, A. Polcar, “Effect of various ignition timings on combustion process and performance of gasoline engine,” Acta Univ. Agric. Silvic. MendelianaeBrun., vol. 65, no. 2, pp. 545–554, April 2017.

XV. M. Aldhaidhawi, M. Naji, K. A. Subhi, “Numerical study of combustion characteristic, performance and emissions of a SI engine running on gasoline, ethanol and LPG” Teat Engineering and Management, vol. 82, pp. 3559-3565, January-February 2020

XVI. M. Gumus, “Effects of volumetric efficiency on the performance and emissions characteristics of a dual fueled (gasoline and LPG) spark ignition engine,” Fuel Processing Technology, vol. 92, no. 10, 1862-1867, October 2011.

XVII. M. Najee, M. Aldhaidhawi, O. Khudhair, “Study on performance and emissions of SI engine fueled by different fuels” ARPN Journal of Engineering and Applied Sciences, vol. 14, no. 8, pp. 1490-1494, April 2019

XVIII. M. Pecqueur, K. Ceustermans, P. Huyskens, D. Savvidis, “Emissions Generated from a Suzuki Liane Running on Unleaded Gasoline and LPG under the Same Load Conditions,” SAE Technical Paper, 2637, Oct. 2008.

XIX. O. I.Awad, R. Mamat, O. M. Ali, N. A. C. Sidik, T. Yusaf, K. Kadirgama, M. Kettner, “Alcohol and ether as alternative fuels in spark ignition engine: A review,” Renewable and Sustainable Energy Reviews, vol. 82, no. 3, PP. 2586-2605, February 2018.

XX. S. Yousufuddina, M. Masoodb, “Effect of ignition timing and compression ratio on the performance of a hydrogen–ethanol fuelled engine,” International journal of hydrogen energy,” vol. 34, no. 16, pp. 6945- 6950, August 2009.

XXI. T. Hu, Y. Wei, S. Liu, L. Zhou, “Improvement of sparkignition (SI) engine combustion and emission during cold start, fueled with methanol/gasoline blends,” Energ& Fuels, vol. 21, pp. 171-175, November 2007.

EFFICIENT MULTI-LEVEL ENCRYPTION PROCEDURE FOR CLOUD SECURITY

Authors:

Sampath Kumar Tallapally, B. Manjula

DOI NO:

https://doi.org/10.26782/jmcms.2020.06.00031

Abstract:

Cloud space to yourself is a one of the most considerable tentative issue in cloud computing as some of the clients are satisfied with existing policies or protocols where as rest of them are quite concerned with the aspects of corresponding security [IV].In order to enhance the security levels in this paper we have proposed a multilevel security scheme that provides more security than that of any type of the existing single level encryption based process. In particular the proposed technique ensures that only pre authorized users can only access the cloud data and the other advantage of our algorithm is faster and safer in multiple directions such as while performing uploading and downloading a specific file [X].

Keywords:

Cloud security,single level encryption,multilevel security scheme,cloud data,

Refference:

I. https://en.wikipedia.org/wiki/Cloud_computing_

II. https://en.wikipedia.org/wiki/Advanced_Encryption_Standard

III. https://en.wikipedia.org/wiki/Elliptic-curve_cryptography

IV. https://en.wikipedia.org/wiki/Hessian_form_of_an_elliptic_curve

V. R Ravi Kumar M Babu Reddy P Praveen, “An Evaluation Of Feature Selection Algorithms In Machine Learning” International Journal Of Scientific & Technology Research Volume 8, Issue 12, December 2019 ISSN 2277-8616,PP. 2071-2074.

VI. T. Sampath Kumar, B. Manjula “Perusing on Cloud Computing and its Security Issues”. International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-9 Issue-2, December, 2019

VII. T. Sampath Kumar, B. Manjula “Security Issue Analysis on Cloud Computing Based System” International Journal of Future Generation Communication and Networking Vol. 12, No. 5, (2019), pp. 143 – 150

VIII. T. Sampath Kumar, B. Manjula, “Asymmetric AES Algorithm for Cloud Security”, International Journal of Future Generation Communication and Networking Vol. 12, No. 5, (2019), pp. 301- 305

IX. R. Ravi Kumar, M. Babu Reddy and P. Praveen, “A review of feature subset selection on unsupervised learning,” 2017 Third International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB), Chennai, 2017, pp. 163-167.doi: 10.1109/AEEICB.2017.7972404.

X. Sanjoli Singla and Jasmeet Singh ,“Cloud Data Security using Authentication and Encryption Technique” by in IJARCET Vol 2, Issue 7, July 2013.

XI. Survey on triple system security in cloud computing by ParulMukhi and Bhawna Chauhan in IJCSMC, Vol. 3, Issue. 4, April 2014.

XII. T. Sampath Kumar,B. Manjula, D. Srinivas,”A New Technique to Secure Data Over Cloud”, Jour of Adv Research in Dynamical & Control Systems, 11-Special Issue, July 2017.

XIII. T. Sampath Kumar, B. Manjula, Mohammed Ali Shaik, Dr. P. Praveen, “A Comprehensive Study on Single Sign on Technique”, International Journal of Advanced Science and Technology (IJAST), ISSN:2005-4238E-ISSN:2207-6360, Vol-127-June-2019.

XIV. Praveen P., Rama B(2020). “An Optimized Clustering Method To Create Clusters Efficiently” Journal Of Mechanics Of Continua And Mathematical Sciences, ISSN (Online): 2454 -7190 Vol.-15, No.-1, January (2020) pp 339-348 ISSN (Print) 0973-8975 https://doi.org/10.26782/jmcms.2020.01.00027.

XV. Praveen., P and Ch. Jayanth Babu. “Big Data Clustering: Applying Conventional Data Mining Techniques in Big Data Environment.” (2019).Innovations in Computer Science and Engineering, Lecture Notes in Networks and Systems 74, ISSN 2367-3370, https://doi.org/10.1007/978-981-13-7082-3_58 Springer Singapore.

LI-FI TECHNOLOGY UTILIZED IN LEVERAGED TO POWER IN AVIATION SYSTEM ENTERTAINMENT THROUGH WIRELESS COMMUNICATION

Authors:

Yerrolla Chanti, Bandi Bhaskar, Nagendar Yamsani

DOI NO:

https://doi.org/10.26782/jmcms.2020.06.00032

Abstract:

Li-Fi-light constancy is like Wi-Fi innovation and it is one of things to come remote correspondence advancements part. The principle capacity of this innovation is to transmit the information by means of light [IX]. This innovation is unspoiled for fast remote correspondence in a limited district, and it offers numerous advantages over Wi-Fi innovation, for example, high transfer speed, convenience, productivity, and wellbeing. As the light speed is prevalent thus the information correspondence speed is additionally quicker in the current framework [X]. Moreover, this innovation can be executed for quick information access for the PCs, and contraptions that will be transmitted during the pillar in a room [IX]. This paper propose Li-Fi innovation utilizing in flying to theater setup through remote correspondence by basically utilizing the divider/perusing sheep it's protected to state that this innovation region of use are wearisome

Keywords:

Aviation,Wall,Reading Lamps,VLC,Interminable,

Refference:

Angayarkanni S 1 , Arthi R 2 , Nancy S 3 , Sandhiya A 4 Assistant Professor1 , Student2, 3, 4, 5 Department Of Electrical And Electronics Engineering TejaaShakthi Institute Of Technology For Women” Underwater Communication Using Li-Fi Technology” Research Article Volume 8 Issue No.3 © 2018 IJESC.
II. Anurag Sarkar1, Prof. Shalabh Agarwal2 , Dr. Asoke Nath3, Department Of Computer Science St. Xavier’s College (Autonomous) Kolkata – India” Li-Fi Technology: Data Transmission Through Visible Light” International Journal Of Advance Research In Computer Science And Management Studies Volume 3, Issue 6, June 2015 Pg. 1-12.
III. Bura Vijay Kumar, YerrollaChanti, NagenderYamsani, SrinivasAluvala, BandiBhaskar “Design A Cost Optimum For 5g Mobile Cellular Network Footing On NFV And SDN”, International Journal Of Recent Technology And Engineering (IJRTE), ISSN: 2277-3878, Volume-8, Issue-2S3, July 2019
IV. Department Of Information Technology, Al Baha University, Al Baha, Kingdom Of Saudi Arabia (KSA) *Corresponding Author: Yusufperwej@Gmail.Com” The Next Generation Of Wireless Communication Using Li-Fi (Light Fidelity) Technology” Journal Of Computer Networks, 2017, Vol. 4, No. 1, 20-29 Available Online At Http://Pubs.Sciepub.Com/Jcn/4/1/3 ©Science And Education Publishing DOI:10.12691/Jcn-4-1-3.
V. Gowtham S U, Gokulamanikandan M, Pavithran P, Gopinath K Department Of CSE, Panimalar Institute Of Technology, Chennai, Tamilnadu, India” Interactive Voice & IOT Based Route Navigation System For Visually Impaired People Using Lifi” International Journal Of Scientific Research In Computer Science, Engineering And Information Technology © 2017 IJSRCSEIT | Volume 2 | Issue 2 | ISSN : 2456-3307.
VI. Hemachandran K1, Justus Rabi B2, SS Darly3 1Associate Professor, ECE, Visvesvaraya College Of Engineering & Technology, Hyderabad, Telungana, India” Elegant Billing System Using Light Fidelity Module” International Journal Of Electrical Electronics & Computer Science Engineering Volume 4, Issue 4 (August, 2017) | E-ISSN : 2348-2273 | P-ISSN : 2454-1222 Available Online At Www.Ijeecse.Com
VII. Khandal1, Sakshi Jain21,2Poornima College Of Engineering, Jaipur (Rajasthan)” Li-Fi (Light Fidelity): The Future Technology In Wireless communication” International Journal Of Information & Computation Technology. ISSN 0974-2239 Volume 4, Number 16 (2014), Pp.© International Research Publications Househttp://Www. Irphouse.Com
VIII. Praveen P., Rama B(2020). “An Optimized Clustering Method To Create Clusters Efficiently” Journal Of Mechanics Of Continua And Mathematical Sciences,ISSN (Online) : 2454 -7190 Vol.-15, No.-1, January (2020) pp 339-348 ISSN (Print) 0973-8975 ,https://doi.org/10.26782/jmcms.2020.01.00027.
IX. NagendarYamsani, Bura Vijay Kumar, SrinivasAluvala, Mahesh Dandugudum, G. Sunil Reddy, “An Improved Load Balancing In MANET Using On-Demand Multipath Routing Protocol” , International Journal Of Engineering &Technology, 7 (1.8) (2018) Pp.222-225.
X. P.Kumara Swamy, Dr. C. V. Guru Rao, Dr. V. Janaki, “Functioning Of Secure Key Authentication Scheme In” In International Journal Of Pure And Applied Mathemat, Volume 118, Issue 14, Page No(S) 27 – 32, MAR. 2018, [ISSN(Print):1314-3395]
XI. P. Koteswara Rao1, M.Prathibha 2, K. Sai Prasanna3 , I.Sowjanya4 1assistant Professor, Dept Of Ece,Andhra Loyola Institute Of Engineering And Technology “Intra Vehicular Communication Byusing Lifi For Pre-Empitive collision Avoidance” International Journal Of Emerging Trends & Technology In Computer Science (Ijettcs) Web Site: Www.Ijettcs.Org Email: Editor@Ijettcs.Org Volume 6, Issue 2, March – April 2017 Issn 2278-6856.
XII. Srinivas Aluvala, K. Raja Sekar,, Deepika Vodnala, “A Novel Technique For Node Authentication In Mobile Ad-Hoc Networks” In Elsevier – Perspectives In Science, Volume 8, Issue 1, Page No(S) 680 -682, SEP. 2016, [ISSN(Print):2213-0209],
XIII. Yerrolla Chanti, Dr. K. Seena Naik2, Rajesh Mothe3, Nagendar Yamsani4, Swathi Balija5” A Modified Elliptic Curve Cryptography Technique For Securing Wireless Sensor Networks” International Journal Of Engineering &Technology 2018.
XIV. Yerrolla Chanti, Kothanda Raman, K. Seenanaik, Dandugudum Mahesh, B.Bhaskar” An Enhanced On Bidirectional LI-FI Attocell Access Point Slicing And Virtualization Using Das2 Conspire” International Journal Of Recent Technology And Engineering (IJRTE) ISSN: 2277-3878, Volume-8, Issue-2S3, July 2019

PERIODIC SOLUTION OF THE NONLINEARJERK OSCILLATOR CONTAINING VELOCITY TIMES ACCELERATION-SQUARED: ANITERATION APPROACH

Authors:

B. M. IkramulHaque, Md. Zaidur Rahman, Md. Iqbal Hossain

DOI NO:

https://doi.org/10.26782/jmcms.2020.06.00033

Abstract:

Haque’s iteration approach has been applied to obtain analytical solution of the nonlinear jerk equation containing velocity times acceleration-squared. We have used truncated Fourier series by taking different numbers of harmonics for different iteration step. The obtained solutions give more accurate result than others and very nearer to the exact solution.

Keywords:

Jerk equation, nonlinear oscillator,Iteration Method,Truncated Fourier series,

Refference:

I. Alam, M.S., Haque, M.E. and Hossain M.B.,“A new analytical technique to find periodic solutions of nonlinear systems” Int. J. Nonlinear Mech., vol. 24, pp. 1035-1045, 2007.

II. Beléndez, A., Pascual, C., Ortuno, M., Beléndez, T. and Gallego, S., “Application of a modified He’s homotopy perturbation method to obtain higher-order approximations to a nonlinear oscillator with discontinuities” Nonlinear Anal. Real World Appl, vol.10 (2), pp. 601-610, 2009.
III. Gottlieb, H.P.W.,“Question #38. What is the simplest jerk function that gives chaos?” American Journal of Physics, vol.64, pp. 525, 1996.
IV. Gottlieb, H.P.W.,“Harmonic balance approach to periodic solutions of nonlinear jerk equation” J. Sound Vib., vol. 271, pp. 671-683, 2004.
V. Gottlieb, H.P.W.,“Harmonic balance approach to limit cycle for nonlinear jerk equation” J. Sound Vib., vol. 297, pp. 243-250, 2006.
VI. Hu, H.,“Perturbation method for periodic solutions of nonlinear jerk equations” Phys. Lett. A, vol. 372, pp. 4205-4209, 2008.
VII. Hu, H., Zheng, M.Y. and Guo, Y.J.,“Iteration calculations of periodic solutions to nonlinear jerk equations”Acta Mech., vol. 209, pp. 269-274, 2010.
VIII. Haque, B.M.I., Alam, M.S. and MajedurRahmam, M.,“Modified solutions of some oscillators by iteration procedure” J. Egyptian Math. Soci., vol. 21, pp. 68-73, 2013.
IX. Haque, B.M.I., A “New Approach of Iteration Method for Solving Some Nonlinear Jerk Equations” Global Journal of Science Frontier Research Mathematics and Decision Sciences, vol. 13, pp. 87-98, 2013.
X. Haque, B.M.I.,“A New Approach of Mickens’ Extended Iteration Method for Solving Some Nonlinear Jerk Equations” British journal of Mathematics & Computer Science, vol. 4, pp. 3146-3162, 2014.
XI. Haque, B.M.I.,BayezidBostami M., AyubHossain M.M., Hossain M.R. and Rahman M.M.,“Mickens Iteration Like Method for Approximate Solution of the Inverse Cubic Nonlinear Oscillator” British journal of Mathematics & Computer Science, vol. 13, pp. 1-9, 2015.
XII. Haque, B.M.I., AyubHossain M.M., BayezidBostami M. and Hossain M.R.,“Analytical Approximate Solutions to the Nonlinear Singular Oscillator: An Iteration Procedure” British journal of Mathematics & Computer Science, vol. 14, pp. 1-7, 2016.
XIII. Haque, B.M.I.,Asifuzzaman M. and KamrulHasam M.,“Improvement of analytical solution to the inverse truly nonlinear oscillator by extended iterative method” Communications in Computer and Information Science, vol. 655, pp. 412-421, 2017.
XIV. Haque, B.M.I., Selim Reza A.K.M. andMominurRahman M.,“On the Analytical Approximation of the Nonlinear Cubic Oscillator by an Iteration Method” Journal of Advances in Mathematics and Computer Science, vol. 33, pp. 1-9, 2019.
XV. Haque, B.M.I. and AyubHossain M.M.,“A Modified Solution of the Nonlinear Singular Oscillator by Extended Iteration Procedure” Journal of Advances in Mathematics and Computer Science, vol. 34, pp. 1-9, 2019.
XVI. Leung, A.Y.T. and Guo, Z.,“Residue harmonic balance approach to limit cycles of nonlinear jerk equations” Int. J. Nonlinear Mech., vol. 46, pp. 898-906, 2011.
XVII. Mickens, R.E.,“Comments on the method of harmonic balance” J. Sound Vib., vol. 94, pp. 456-460, 1984.
XVIII. Ma, X., Wei, L. and Guo, Z.,“He’s homotopy perturbation method to periodic solutions of nonlinear jerk equations” J. Sound Vib., vol. 314, pp. 217-227, 2008.
XIX. Mickens, R.E.,“Iteration Procedure for determining approximate solutions to nonlinear oscillator equation” J. Sound Vib., vol. 116, pp. 185-188, 1987.
XX. Nayfeh, A.H.,“Perturbation Method” John Wiley & Sons, New York, 1973.
XXI. Nayfeh, A.H. andMook, D.T.,“Nonlinear Oscillation” John Wiley & Sons, New York, 1979.
XXII. Ramos, J.I.,“Analytical and approximate solutions to autonomous, nonlinear, third order ordinary differential equations” Nonlinear Anal. Real., vol. 11, pp. 1613-1626, 2010.
XXIII. Ramos, J.I.,“Approximate methods based on order reduction for the periodic solutions of nonlinear third-order ordinary differential equations” Appl. Math. Comput., vol. 215, pp. 4304-4319, 2010.
XXIV. Ramos, J.I. and Garcia-Lopez,“A Volterra integral formulation for determining the periodic solutions of some autonomous, nonlinear, third-order ordinary differential equations” Appl. Math. Comput., vol. 216, pp. 2635-2644, 2010.
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XXVI. Wu, B.S., Lim, C.W. and Sun, W.P.,“Improved harmonic balance approach to periodic solutions of nonlinear jerk equations” Phys. Lett. A, vol. 354, pp. 95-100, 2006.
XXVII. Zheng, M.Y., Zhang, B.J., Zhang, N., Shao,X.X. and Sun, G.Y.,“Comparison of two iteration procedures for a class of nonlinear jerk equations”Acta Mech. vol. 224,pp. 231-239, 2013.

SECURING AND MANAGING ARMY CANTONMENT IN INDIA USING INTERNET OF THINGS

Authors:

Tarun Kumar, Pawan Kukreja, Dharmender Singh Kushwaha, Sanjeev Pippal

DOI NO:

https://doi.org/10.26782/jmcms.2020.06.00034

Abstract:

Internet of Things (IoT) is newfangled area of development that can lead to tremendous efficiency improvement in terms of expensive manpower saving, cost cutting, round the clock availability and modular structure to continuously improve processes and things. This paper proposes an application of the IoT in security and management of the army cantonment area. In the context of security, this paper proposes three kind of integrated applications. The Access control system ensures entry of the authorized vehicles using RFID and Automatic Number Plate Recognition techniques. Intrusion detection based on passive infrared, camera and thermal imaging is proposed at the next level. Gun fire detection in residential blocks of the army campus by using sound sensors ensures any intrusion, attack or any other undue situation. Apart from security, this paper also proposes intelligent use of water flow sensors for smart monitoring of underground drainage and water level sensors to reduce the wastage of water in tank overflow conditions. The proposed system is reliable, efficient in terms of accuracy, response time of the various modules, round the clock capability and economical in terms of operational and maintenance cost.

Keywords:

Vehicle detection,Automatic number plate detection,Radio frequency identification, Sensors,

Refference:

I. Cha, J.-R. and Kim, J.-H. Dynamic framed slotted ALOHA algorithms using fast tag estimation method for RFID system. Proc. IEEE CCNC, 2006, 768–772.
II. Ghayvat H., Mukhopadhyay S., Gui X., Suryadevara N. (2015). WSN-and IOT-based smart homes and their extension to smart buildings. Sensors. 4;15(5):10350-79.
III. http://indianexpress.com/article/india/india-news-india/pathankot-terror-attack-something-seriously-wrong-with-our-security-establishment-parliament-panel/.
IV. http://pascal.inrialpes.fr/data/human/ (2018)
V. http://www.firstpost.com/india/jammu-attack-by-entering-fortified-sunjuwan-army-camp-from-rear-afzal-guru-squad-exploited-security-lapse-4344775.html (2018)
VI. http://www.sengpielaudio.com/calculator-SoundAndDistance.htm (2018)
VII. Iamsa-at S and HorataP.. Handwritten Character Recognition Using Histograms Of Oriented Gradient Features in Deep Learning of Artificial Neural Network. In IEEE International Conference on IT Convergence and Security (ICITCS), pp. 1-5.
VIII. Krishnan, D., Muthaiah, R., Tapas, A., &Kannan, K. (2018). Evaluation of Local Feature Detectors for the Comparison of Thermal and Visual Low Altitude Aerial Images. Defence Science Journal, 68(5), 473-479. https://doi.org/10.14429/dsj.68.11233
IX. Kumar T. &Kushwaha, D. S. An Intelligent Reconnaissance Framework for Homeland Security. Def. Sci. J. 69, 4 (2019).doi: https://doi.org/10.14429/dsj.67.10286
X. Kumar, T., Gupta, S. &Kushwaha, D. S. An Efficient Approach for Automatic Number Plate Recognition for Low Resolution Images. in The fifth International Conference on Network, Communication and Computing (ICNCC) 2016 53–57.
XI. Maguire, Y. and Pappu, R. An optimal Q-algorithm for the ISO 18000-6C RFID protocol. IEEE transactions on automation science and engineering, 2009 6, 1, 16–24.
XII. Maisonneuve M. Ortiz et al.(2014). The cluster between Internet of Things and social networks: Review and research challenges, IEEE Internet Things J. , vol. 1, no. 3, pp. 206–215, Jun. 2014
XIII. Rao Y. R. Automatic smart parking system using Internet of Things (IOT). Int J EngTechnolSci Res. 2017 May;4(5).
XIV. Rathore M. M., Ahmad A., Paul A., Rho S. Urban planning and building smart cities based on the internet of things using big data analytics. Computer Networks. 2016 Jun 4;101:63-80.
XV. Singh, D. K. &Kushwaha, D. S. Automatic Intruder Combat System: A way to Smart Border Surveillance. Def. Sci. J. 67, 50 (2017).doi: https://doi.org/10.14429/dsj.67.10286
XVI. Verma, K., Kumar, A., &Ghosh, D. (2018). Robust Stabilised Visual Tracker for Vehicle Tracking. Defence Science Journal, 68(3), 307-315. https://doi.org/10.14429/dsj.68.12209
XVII. Wensheng S. and Chenmin J. A Novel Dynamic Frame Slotted ALOHA Algorithm for Anti-Collision in RFID Systems in Journal of Information and control, 2012, (2): 233-237.
XVIII. Yun M., Yuxin B. Research on the architecture and key technology of Internet of Things (IoT) applied on smart grid. InAdvances in Energy Engineering (ICAEE), 2010 IEEE International Conference on Jun 19 (pp. 69-72).
XIX. Zanella A. et al. (2014). Internet of Things for smart cities, IEEE Internet Things J. , vol. 1, no. 1, pp. 22–32.

DUAL SINK BASED ROUTING SCHEME FOR RELIABLE DATA DELIVERY AND LOS COMMUNICATION IN WBANS

Authors:

Ilyas Khan, Majid Ashraf, Asif Nawaz, Rehan Ali Khan, M.Habib Ullah, Wisal Khan, Sheeraz Ahmed

DOI NO:

https://doi.org/10.26782/jmcms.2020.06.00035

Abstract:

The architecture of WBANs consists of small nodes which are fitted on the body of human or it may be implanted inside body to investigate and analyze and sense data like monitoring body temperature, blood pressure, heart rate and glucose level checking etc. For efficient design and development of WBANs, which ensure reliability and efficiency the knowledge of system and its components are necessary. WBANs must be capable to support lower energy, high data rate, reliability, Quality of Service (QoS) and minimum interference for the consideration of vast applications of WBANs. In WBANs there is a need of proactive management because it is related to more reliable communication. In this research work we are trying to provide a comprehensive review of state of the art routing protocols for WBANs. After the thorough analysis and investigation of different routing protocols, we conclude that there are many good schemes to overcome and resolve the issues of routing in WBANs. But still some of the issues need to be resolved. A new routing protocol for WBANs is developed called DSBAN. In this scheme we considered the performance metrics in terms of the already available schemes SIMPLE, and LAEEBA and see the effects in terms of energy efficiency, Networks lifetime and path-loss. The results show that the scheme DSBAN is significantly showing improved performance than the other two schemes under consideration. The reason is that the scheme considers those positive features of SIMPLE and LAEEBA which help us in the design of the new scheme.

Keywords:

WBAN,LoS communication,Routing protocol, Quality of Service,

Refference:

I Ahmed, S., et al. “LAEEBA: Link aware and energy efficient scheme for body area networks.” 2014 IEEE 28th International Conference on Advanced Information Networking and Applications.IEEE, 2014.

II Akram, S., et al. “The-fame: Threshold based energy-efficient fatigue measurement for wireless body area sensor networks using multiple sinks.”Broadband and Wireless Computing, Communication and Applications (BWCCA), 2013 Eighth International Conference on. IEEE, 2013.

III Ansari, Hannan, Sachin Kumar Patel, and Sachida Nanda Barik. “Survey on Wireless Sensor Networks.” (2014).

IV Ari, Ado Adamou Abba, et al. “Concepts and evolution of research in the field of wireless sensor networks.” arXiv preprint arXiv:1502.03561 (2015)

V Bahanfar, Saeid, et al. “Reliable communication in wireless body area sensor network for health monitoring.” arXiv preprint arXiv: 1112.0393 (2011).

VI Braem, Bart, et al. “The need for cooperation and relaying in short-range high path loss sensor networks.” Sensor Technologies and Applications, 2007.SensorComm 2007.International Conference on.IEEE, 2007.

VII Braem, Bart, et al. “Improving reliability in multi-hop body sensor networks.”Sensor Technologies and Applications, 2008.SENSORCOMM’08.Second International Conference on.IEEE, 2008.

VIII Crosby, Garth V., et al. “Wireless body area networks for healthcare: a survey.” International Journal of Ad Hoc, Sensor & Ubiquitous Computing 3.3 (2012): 1.

IX Ehyaie, Aida, MassoudHashemi, and PejmanKhadivi. “Using relay network to increase life time in wireless body area sensor networks.”World of Wireless, Mobile and Multimedia Networks & Workshops, 2009.WoWMoM 2009.IEEE International Symposium on a. IEEE, 2009.

X Elias, Jocelyne, and Ahmed Mehaoua. “Energy-aware topology design for wireless body area networks.”2012 IEEE international conference on communications (ICC).IEEE, 2012.

XI Heinzelman, Wendi Rabiner, AnanthaChandrakasan, and HariBalakrishnan. “Energy-efficient communication protocol for wireless microsensor networks.”System sciences, 2000.Proceedings of the 33rd annual Hawaii international conference on.IEEE, 2000.Yick, Jennifer, Biswanath Mukherjee, and DipakGhosal. “Wireless sensor network survey.” Computer networks 52, no. 12 (2008): 2292-2330.

XII Javaid, Nadeem, et al. “Measuring fatigue of soldiers in wireless body area sensor networks.” Broadband and Wireless Computing, Communication and Applications (BWCCA), 2013 Eighth International Conference on. IEEE, 2013.

XIII Khan, Z. A., et al. “Effect of packet inter-arrival time on the energy consumption of beacon enabled MAC protocol for body area networks.”Procedia Computer Science 32 (2014): 579-586.
XIV Monsef, Ehsan, et al. “Managing Quality of Service in Wireless Body Area Networks using CoAP.” 2016 IEEE International Conference on Electro Information Technology (EIT).IEEE, 2016.

XV Nadeem, Adnan, et al. “Application specific study, analysis and classification of body area wireless sensor network applications.” Computer Networks 83 (2015): 363-380.

XVI Nadeem, Q., et al. “Simple: Stable increased-throughput multi-hop protocol for link efficiency in wireless body area networks.” Broadband and Wireless Computing, Communication and Applications (BWCCA), 2013 Eighth International Conference on. IEEE, 2013

XVII Ruprecht, David J. “Body Area Networks and Body Sensor Networks.”Wireless Network 17 (2011): 1-18.

XVIII Tauqir, Anum, et al. “Distance aware relaying energy-efficient: Dare to monitor patients in multi-hop body area sensor networks.” Broadband and Wireless Computing, Communication and Applications (BWCCA), 2013 Eighth International Conference on. IEEE, 2013.

XIX Tavli, Bulent, et al. “A survey of visual sensor network platforms.”Multimedia Tools and Applications 60.3 (2012): 689-726.

XX Wang, Pengyu, et al. “Survey on application of wireless sensor network in smart grid.” Procedia Computer Science 52 (2015): 1212-1217.Ansari, Hannan, Sachin Kumar Patel, and Sachida Nanda Barik. “Survey on Wireless Sensor Networks.” (2014).

XXI Yick, Jennifer, Biswanath Mukherjee, and DipakGhosal. “Wireless sensor network survey.” Computer networks 52.12 (2008): 2292-2330.

XXII Zhang, Mi, and Alexander A. Sawchuk. “Human daily activity recognition with sparse representation using wearable sensors.” IEEE journal of Biomedical and Health Informatics 17.3 (2013): 553-560.

XXIII Zhou, Gang, et al. “Bodyqos: Adaptive and radio-agnostic qos for body sensor networks.” INFOCOM 2008.The 27th Conference on Computer Communications.IEEE.IEEE, 2008.

COVID-19 IN INDIA AND SIR MODEL

Authors:

Asish Mitra

DOI NO:

https://doi.org/10.26782/jmcms.2020.07.00001

Abstract:

In the present numerical investigation, the epidemic patterns of Covid-19 in India is studied from a mathematical modeling perspective. The study is based on the simple SIR (Susceptible-Infectious-Recovered) deterministic compartmental model. It is analyzed fully and then calibrated against publicly available epidemiological data from late January until 10 July 2020 for interpreting the transmission dynamics of the novel coronavirus disease (COVID-19) in India. The purpose of this study is to give a tentative prediction of the epidemic peak and sizes in our country.

Keywords:

COVID-19,India,SIR Model,Parameter Estimation,Simulation,

Refference:

An Introduction to Mathematical Epidemiology by Maia Martcheva, Springer
II. An Introduction to Mathematical Modeling of Infectious Diseases, Michael Y. Li, Springer.
III. Effect of weather on COVID-19 spread in the US: A prediction model for India in 2020 S Gupta, G S Raghuwanshi , A Chanda, Science of the Total Environment, 728 (2020)
IV. https://data.humdata.org/dataset/novel-coronavirus-2019-ncov-cases.
V. Kermack WO, McKendrick AG. A contribution to the mathematical theory of
VI. Rajesh Ranjan, The Ohio State University, Predictions for COVID-19 outbreak in
VII. Solving applied mathematical problems with MATLAB / DingyuXue, Chapman & Hall/CRC.
VIII. United Nations. Department of Economic and Social Affairs; Population Dynamics https://population.un.org/wpp/Download/Standard/Population/ as on 20 May 2020.
IX. Ward, Alex (24 March 2020). “India’s coronavirus lockdown and its looming crisis, explained” (http s://www.vox.com/2020/3/24/21190868/coronavirus-india-modi-lockdown-kashmir).

MARKOV PROCESS AND DECISION ANALYSIS

Authors:

R. Sivaraman

DOI NO:

https://doi.org/10.26782/jmcms.2020.07.00002

Abstract:

The need of proper medical diagnosis and treatment has been need of the day to deal with various infections caused by viruses and micro-organisms. To prevent the spread of the disease we need proper scientific approach and methods in place. This paper suggests one such method using Markov Process technique, in particular deciding how many patients should be allocated to respective doctors in a hospital.

Keywords:

Markov Process,Markov Decision Process,Transition Probabilities, Transition Matrix, Diagonalization of a matrix,, Equilibrium Distribution ,

Refference:

I 49(10):1021–1025, 1998.

II Amanda A. Honeycutt, James P. Boyle, Kristine R. Broglio, Theodore J. Thompson, Thomas J. Hoerger, Linda S. Geiss, and K. M. Venkat Narayan, A dynamic Markov model for forecasting diabetes prevalence in the United States through 2050. Health Care Management

III Behavioral Sciences, pages 9242–9250, 2004.

IV Chih-Ming Liu, Kuo-Ming Wang, and Yuh-Yuan Guh. A Markov chain model for medical

V Distribution under treatment. Mathematical and Computer Modeling, 19(11):53–66, 1994.

VI For discrete-time longitudinal data on human mixed-species infections. In Some Mathematical

VII J. E. Cohen and B. Singer. Malaria in Nigeria: Contrained continuous-time Markov models

VIII L. Billard. Markov models and social analysis, International Encyclopedia of the Social and

IX Questions in Biology, pages 69–133. Providence: American Mathematical Society, 1979.

X Record analysis. The Journal of the Operational Research Society, 42(5):357–364, 1991.

XI S. I. McClean, B. McAlea, and P. H. Millard. Using a Markov reward model to estimate

XII Science, 6:155–164, 2003.

XIII Spend-down costs for a geriatric department. The Journal of the Operational Research Society,

XIV Y. W. Tan. First passage probability distributions in Markov models and the HIV incubation

[0,1] TRUNCATED LOMAX –INVERTED GAMMA DISTRIBUTION WITH PROPERTIES

Authors:

Jumana A. Altawil, Saba N. Al-Khafaji, Ahmed HadiHussain, Sameer Annon Abbas

DOI NO:

https://doi.org/10.26782/jmcms.2020.07.00003

Abstract:

We proposed [0,1] truncated Lomax –Inverted Gamma ([0,1] TLIGD) distribution build on [0,1] truncated Lomax ([0,1] TLD) distribution. General expressions for the statistical properties are obtained, also The Shannon entropy , Relative entropy functions and Stress- Strength model of the ([0,1] TLIGD) are presented

Keywords:

[0,1] TLIGD,stress strength model, Shannon entropy and Relative entropy functions,

Refference:

I. Abid, Salah , K. Abdulrazak, Russul, “[0, 1] truncated fréchet-gamma and inverted gam-ma distributions”, International Journal of Scientific World , 2017.
II. Eugene, N., Lee, C., & Famoye, F.,“Beta-normal distribution and its applications. Communications in Statistics-Theory and methods”,vol. 31(4), pp: 497-512, 2002.
III. Gradshteyn, I. S., & Ryzhik, I. M., “Table of integrals, series, and products”: Academic press,2014.
IV. Gupta, A. K., & Nadarajah, S., “On the moments of the beta normal distribution.Communications in Statistics-Theory and methods”, vol. 33(1), pp: 1-13, 2005.
V. Jamjoom, A., & Al-Saiary, Z., “Computing the moments of order Statistics from independent nonidentically distributed exponentiated Frechet variables”. Journal of Probability and Statistics, 2012.
VI. Jones, M., “Families of distributions arising from distributions of order statistics”. Test, vol. 13(1),pp :1- 43,2004.
VII. Maria do Carmo, S. L., Cordeiro, G. M., & Ortega, E. M., “A new extension of the normal distribution. Journal of Data Science”, vol. 13(2), pp: 385-408,2014.

AADHAAR ENABLED ELECTRONIC VOTING MECHANISM

Authors:

Maisagalla Gopal, S. Umamaheshwar, Kommabatla Mahender

DOI NO:

https://doi.org/10.26782/jmcms.2020.07.00004

Abstract:

Aadhaar based identification systems are gaining momentum and it isused in several authentication mechanisms. In many democratic countries, the electoral system is still in its juvenile stage and operating in a manual mechanism which consumes huge resources for every voting. In this work, we propose a mechanism which uses Aadhaar based identification to enable a voter to vote. The connection between the voting machine and Aadhaar database is fully secured and encrypted. To avoid intentional hacking, the whole system is computerized and does not require human intervention.

Keywords:

Refference:

I. Ankita R Kasliwal, Jaya S. Gadekar, Manjiri A. Lavadkar, Pallavi K. Thorat and Prapti Deshmukh,“Aadhar Based Election Voting System”IOSR Journal of Computer Engineering, pp.18-21, 2017.
II. K. Dinakaran, P. Aravind Kumar, E. Bagavathi, M. Kathiresh Kumar, R. Madhankumar,“Smart Electronic Voting Machine Using Raspberry Pi”, International Journal of AdvancedResearch in Electrical, Electronics and Instrumentation Engineering, Vol. No. 8, pp. 829-834, March 2019.
III. Kolluru Venkata Nagendra, Palem Chandrakala, Palicherla Anusha, Dampuru Ramesh,“Implementing Aadhar Voting System in Elections Using Raspberry Pi”, InternationalJournal of Scientific Research and Review, Vol. No.7, pp. 500-507, 2018.
IV. Latha V. and Satheesh Thirumalal, “Aadhar Based Electronic Voting System andProviding Authentication on Internet of Things”, International Journal of Engineering andManufacturing Science, Vol. No. 8, pp.102-108, 2018.
V. Lingamallu Naga Srinivas and K. Srinivasa Rao, “Aadhaar Card Voting System”, Proceedings of International Conference on Computational Intelligence and Data Engineering, Vol. No. 9, pp. 159 -172, December 2018.
VI. N. N Nagamma, M. V. Lakshmaiah and T. Narmada, “Aadhar based Finger print EVMSystem”,International Journal of Electronics Engineering Research,Vol. No. 9, pp. 923-930,2017.
VII. R. Murali Prasad, Polaiah Bojja and Madhu Nakirekanti, “Aadhar based ElectronicVoting Machine using Arduino”, International Journal of Computer Applications, Vol. No.145, pp. 39-42, July 2016.
VIII. Rakesh S. Raj, Reshma, Madhushree and Bhargavi, “An Online Voting System Using Biometric Fingerprint and Aadhaar Card”, International Journal of Computing and Technology, Vol. No. 1, pp.87-92, May 2014.
IX. Sneha S.Lad, Pranav N.Tonape, Rohit S.Bhosale, Jayesh A.Shingole, Vinayak S.Kumar, “E-Voting and Presentee Muster Using Raspberry Pi 2 Modules”, International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering, Vol. No. 4, pp. 475-482, May 2016.
X. Syed Mahmud Hasan, Arafa Mohd Anis, Hamidur Rahman, Jennifer Sherry Alam, Sohel Islam Nabil and Md KhalilurRhaman, “Development of Electronic Voting Machine with the Inclusion of Near Field Communication ID cards and Biometric fingerprint identifier”,17th International Conference on Computer and Information Technology, pp. 383-387, 2014.
XI. Tabish Ansari, Brijesh Chaurasia, Niraj Kumar, Nilesh Yadav, SonaliSuryawanshi, “Online Voting System linked with Aadhaar Card”, International Journal of Advanced Research in Computer and Communication and Communication Engineering, Vol. No. 6, pp. 204-207, September 2017.

ESTIMATION TYPES OF FAILURE FOR THERMO-ELECTRIC UNIT BY USING ARTIFICIAL NEURAL NETWORK (ANN)

Authors:

Asmaa Jamal Awad, Ahmed Abdulrasool Ahmed, Osamah Abdallatif

DOI NO:

https://doi.org/10.26782/jmcms.2020.07.00005

Abstract:

Frequent failure in production systems is one of the most important problems facing maintenance planners. In this paper, the methodology for estimating failure in an electrical energy production system has been proposed.Consisting of a number of related sub-systems, respectively, failure of any one causes the rest to stop producing.Operating data were collected and the type of failure identified, which was classified into three types (mechanical failure, electrical failure, and control failure). The software (Matlab) was used in generating and training an artificial neural network (ANN) to estimate the type of failure, through the data collected for each sub-system of the unit under study, use 90% of the data for training, 5% for testing, and 5% for valuation. The target matrix was built and trained, with a mean square error (MSE) its(6.54 E-16), and regression (91%), and adopted to estimate the type of future failure for subsequent years(2019),conformance results were for the subsequent year between (82%-87%) for all the subsystems. Using the artificial neural network, failure types were estimated for another subsequent year (2020), the failure ratios were for subsystems for every ten days during the year of estimation, were (33%) for the generator, (22%) for the boiler, (31%) for the turbine, and (13%) for the condenser. High percentages, which can be reduced by taking advantage of the proposed methodology that gave an understanding of the type of failure, the time it occurred, and the location of the failure, by building an overlapping preventive maintenance plan whose application is approved in reducing the failuretimes of the unit under study.The proposed methodology can also be applied to all other systems of different production

Keywords:

Matlab software, Generator,Artificial Intelligent (AI),

Refference:

I. Devika Chhachhiya, Amita Sharma, Manish Gupta “Case Study on Classification of Glass Using Neural Network Tool in MATLAB” International Journal of Computer Applications, 0975 – 8887),(2014).
II. D. Bose, G. Ghosh, K. Mandal, S.P. Sau4 and S. Kunar “Measurement and Evaluation of Reliability, Availability and Maintainability of a Diesel Locomotive Engine” International Journal of Engineering Research and Technology, Volume 6, Number 4,pp. 515-534, 2003.

III. Emilia Sipos, Laura-Nicoleta Ivanciu”Failure Analysis and Prediction Using Neural Networks in the Chip Manufacturing Process “ResearchGate, DOI: 10.1109/ISSE.2017.8000931, May 2017

IV. Erdi Tosun, Ahmet C¸ alık”Failure load prediction of single lap adhesive joints using artificial neural networks”Alexandria Engineering Journal vol. 55, pp1341–1346,2016
V. Farhad Hooshyaripora, Ahmad Tahershamsib, and Kourosh Behzadian”Estimation of Peak Outflow in Dam Failure Using Neural Network Approach under Uncertainty Analysis” Pleiades Publishing, Vol. 42, No. 5, 2015
VI. Gustavo Scalabrini Sampaio, Arnaldo Rabello de Aguiar Vallim Filho,Leilton Santos da Silva and Leandro Augusto da Silva” Prediction of Motor Failure Time Using An Artificial Neural Network” Sensors, 19, 4342; doi:10.3390/s19194342, 2019
VII. Laurene V. Fausett, “Fundamentals of Neural Networks: Architecture, Algorithm, and Application”, Florida Institute of Technology, First Edition, December, 1993.
VIII. Mahdi Saghafi , Mohammad B. Ghofrani “Real-time estimation of break sizes during LOCA in nuclear power plants using NARX neural network” Nuclear Engineering and Technology doi.org/10.1016/j.net.2018.11.017
IX. M. Goya-Martinez, “The Emulation of Emotions in Artificial Intelligence,” Emotions, Technology, and Design. pp. 171–186, 2016
X. Walter, E.; Pronzato L., “Identification of Parametric Models from Experimental Data”, London, England: Springer-Verlag, 1997.

COPRAS BASED CLUSTERING STRATEGY TOWARD ENERGY-EFFICIENT IOT-CLOUD TRANSMISSION

Authors:

Arpita Biswas, Abhishek Majumdar, K. L. Baishnab

DOI NO:

https://doi.org/10.26782/jmcms.2020.07.00006

Abstract:

IoT is a globally accepted smart technology that has the ability to connect each and almost every physical devices through the network. It acts as a bridge between cloud environment and physical environment. It is mainly used to connect the hardware devices like sensors, actuators, storage, hardware, and software to acquire or exchange data. These devices collect the information from the physical world and convert this into useful information that can help in decision making. Since IoT connects everything to the network, so it may face the problem of a large amount of energy loss. In this respect, this paper mainly focuses on reducing the energy loss problem and designing of an energy efficient data transfer scenario between cloud and IoT devices. For this reason, a Complex Proportional Assessment (COPRAS) based clustering approach has been proposed in this work to select the cluster premier effectively and form the set of best clusters for maximizing the network lifetime. The proposed work deals with data transmission model between IoT and cloud that confirms the improvement in energy efficiency, network lifetime, and latency. Furthermore, the sensitivity analysis has also been carried out and satisfactory results has been obtained.

Keywords:

Cloud Computing, Clustering, MCDM, IoT,

Refference:

I. A. Majumdar, T. Debnath, S. K. Sood, K. L. Baishnab, “Kyasanur forest disease classification framework using novel extremal optimization tuned neural network in fog computing environment”, Journal of medical systems, Springer, vol. 42, no.10, pp.187, 2018.
II. A. Majumdar, A., Biswas, K. L. Baishnab, S. K. Sood, “DNA Based Cloud Storage Security Framework Using Fuzzy Decision Making Technique”, KSII Transactions on Internet & Information Systems, vol.13, no.7, pp. 3794-3820, 2019.
III. A. Majumdar, N. M. Laskar, A. Biswas, S. K. Sood, K. L. Baishnab, “Energy efficient e-healthcare framework using HWPSO-based clustering approach”, Journal of Intelligent & Fuzzy Systems, IOS Press, vol. 36, no. 5, pp. 3957-3969, 2019.
IV. A. Biswas, A. Majumdar, S. Nath, A. Dutta, K. L. Baishnab, “LRBC: a lightweight block cipher design for resource constrained IoT devices”, Journal of Ambient Intelligence and Humanized Computing, Springer pp.1-15, 2020.
V. A. V. Dhumane and R. S. Prasad, “Fractional Gravitational Grey Wolf Optimization to Multi-Path Data Transmission in IoT”, Wireless Personal Communications, Springer, vol. 102, no. 1, pp. 411-36, 2018.
VI. A. V. Dhumane, R. S. Prasad, and J. R. Prasad, “An optimal routing algorithm for internet of thing enabling technologies”, International Journal of Rough Sets and Data Analysis (IJRSDA), vol. 4, no. 3, pp. 1-16, 2017.
VII. A. Orsino, G. Araniti, L. Militano, J. Alonso-Zarate, A. Molinaro, A. Iera,. “Energy efficient IoT data collection in smart cities exploiting D2D communications”, Sensors, vol. 16, no. 6, p.836, 2016.
VIII. D. Wei, S. Kaplan, H.A. Chan, “Energy efficient clustering algorithms for wireless sensor networks”, In Communications Workshops, 2008. ICC Workshops’ 08. IEEE International Conference on, pp. 236-240, 2008.
IX. G. L. da Silva Fré, J. de Carvalho Silva, F.A. Reis, and L.D.P. Mendes, “Particle Swarm optimization implementation for minimal transmission power providing a fully-connected cluster for the internet of things,” in International Workshop on Telecommunications (IWT), pp. 1–7, 2015.
X. I. Yaqoob, E. Ahmed, I.A.T. Hashem, A.I.A. Ahmed, A. Gani, M. Imran, M. Guizani, “Internet of things architecture: Recent advances, taxonomy, requirements, and open challenges”, IEEE wireless communications, vol. 24, no. 3, pp.10-16, 2017.
XI. J. H. Kwon, M. Cha, S. B. Lee, and E. J. Kim, “Variable-categorized clustering algorithm using fuzzy logic for Internet of things local networks”, Multimedia Tools and Applications, Springer, vol. 78, no.3, pp. 2963-82, 2019.
XII. J. A. Martins, A. Mazayev, N. Correia, G. Schütz, and A. Barradas, “GACN: Self-clustering genetic algorithm for constrained networks”, IEEE Communications Letters, vol. 21, no. 3, pp. 628-31, 2017.
XIII. J.M. Liang, J.J. Chen, H.H. Cheng, Y.C. Tseng, “An energy-efficient sleep scheduling with qos consideration in 3gpp lte-advanced networks for internet of things,” IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 3, no. 1, pp.13-22, 2013.
XIV. J. Tang, Z. Zhou, J. Niu, Q. Wang, “An energy efficient hierarchical clustering index tree for facilitating time-correlated region queries in the Internet of Things”, Journal of Network and Computer Applications, vol. 40, pp.1-11, 2014.
XV. L. Song, K. K. Chai, Y. Chen, J. Loo, S. Jimaa, and J. Schormans, “QPSO-based energy-aware clustering scheme in the capillary networks for Internet of Things systems”, in Wireless Communications and Networking Conference, IEEE, April 2016, pp. 1-6.
XVI. L. Song, K.K. Chai, Y. Chen, J. Schormans, J. Loo, A. Vinel, “QoS-Aware Energy-Efficient Cooperative Scheme for Cluster-Based IoT Systems”, IEEE Systems Journal, vol. 11, no. 3, pp.1447-1455, 2017.
XVII. M. P. K. Reddy and M. R. Babu, “Implementing self adaptiveness in whale optimization for cluster head section in Internet of Things”, Cluster Computing, Springer, pp. 1-12, 2018.
XVIII. M. P. K. Reddy and M. R. Babu, “Energy Efficient Cluster Head Selection for Internet of Things”, New Review of Information Networking, Taylor & Francis, vol. 22, no. 1, pp. 54-70, 2017.
XIX. M. P. K. Reddy and M. R. Babu, “An Evolutionary Secure Energy Efficient Routing Protocol in Internet of Things”, International Journal of Intelligent Engineering and Systems, vol. 10, no. 3, pp. 337-46, 2017.
XX. N. T. Van, T. T. Huynh, and B. An, “An energy efficient protocol based on fuzzy logic to extend network lifetime and increase transmission efficiency in wireless sensor networks”, Journal of Intelligent & Fuzzy Systems, IOS Press, vol. 35, no. 6, pp. 5845-5852, 2018.
XXI. N. Kaur, and S.K. Sood, “An Energy-Efficient Architecture for the Internet of Things (IoT)”, IEEE Systems Journal, vol.11, no.2, pp.796-805, 2017.
XXII. Ö.U. Akgül, B. Canberk, “Self-Organized Things (SoT): An energy efficient next generation network management,” Computer Communications, vol. 74, pp.52-62, 2016.
XXIII. S. K. Singh, M.P. Singh, D.K. Singh, “Energy-efficient homogeneous clustering algorithm for wireless sensor network”, International Journal of Wireless & Mobile Networks (IJWMN), vol. 2, no. 3, pp.49-61, 2010.
XXIV. S. Rani, R. Talwar, J. Malhotra, S.H. Ahmed, M. Sarkar, H. Song, “A novel scheme for an energy efficient Internet of Things based on wireless sensor networks”, Sensors, vol. 15, no. 11, pp.28603-28626, 2015.
XXV. S. D. Muruganathan, D. C. Ma, R. I. Bhasin, A. O. Fapojuwo, “A centralized energy-efficient routing protocol for wireless sensor networks”, IEEE Communications Magazine, vol. 43, no. 3, pp. S8-13, 2005.
XXVI. T. Ayesha, S. Sadaf, D. Sinha, and A. K. Das. “Secure Anti-Void Energy-Efficient Routing (SAVEER) Protocol for WSN-Based IoT Network”, In Advances in Computational Intelligence, pp. 129-142. Springer, Singapore, 2020.
XXVII. Z. Zhou, J. Tang, L.J. Zhang, K. Ning, Q. Wang, “EGF-tree: an energy-efficient index tree for facilitating multi-region query aggregation in the internet of things”, Personal and Ubiquitous computing, vol.18, no.4, pp.951-966, 2014.
XXVIII. A. Majumdar, T. Debnath, K. L. Baishnab, S. K. Sood, “An Energy Efficient e-Healthcare Framework Supported by HEO-µGA (Hybrid Extremal Optimization Tuned Micro-GeneticAlgorithm)”, Information System Frontiers, Springer, 2020, DoI: https://doi.org/10.1007/s10796020-10016-5.
XXIX. A. Majumdar, M. Sharma, “Enhanced information security using DNA cryptographic approach. International Journal of Innovative Technology and Exploring Engineering”, vol.4, no.2, pp. 72-76, 2020.