Archive

Abstract:

Extensions of number of well-known special function such as Beta and Gauss hypergeometric and their properties have been investigated recently by several authors. Our approach is based on the use of Generalized Fractional Calculus (GFC) operators. We aim to investigate the MSM (Marichev-Saigo-Maeda) fractional calculus operator, Caputo-type MSM-fractional differential operator and pathway fractional integral operator of the extended generalized Gauss hypergeometric function. Furthermore, by employing some integral transform on the resulting formulas, we presented some more image formulas. All the results derived here are of general character and can yield a number of (known and new) results in theory of special functions.

Keywords:

Gamma function,Extended generalized beta functions,Generalized hypergeometric functions,Extended generalized hypergeometric functions,Fractional integral operators,Integral transforms,Pathway fractional integral operator,

Refference:

I. A.A.Kilbas, H.M. Srivastava and J.J.Trujillo, “Theory and Applications of
Fractional Differential Equations”, Elsevier, North-Holland Mathematics
Studies 204,Amsterdam, London, NewYork, Tokyo, 2006
II. A.A.Kilbas and N.Sabastian, “Generalized fractional integration of Bessel
function of the first kind”, Integral Transforms special function, Vol. 19, pp.
869-883, 2008
III. A.M. Mathai, “A pathway to matrix-variate gamma and normal densities”,
Linear Algebra and its Application, Vol. 396, pp. 317-328, 2005
IV. A.M.Mathai and H.J.Haubold, “On generalized distributions and pathways”,
Physics & Letters, Vol.372,Issue 12, pp. 2109-2113, 2008
V. A.M. Mathai and H.J. Haubold, “Pathway model, superstatistics” , Tsallis
statistic and a generalized measure of entropy, physica A: Statistical
Mechanics and Its Applications, Vol.375 Issue 1,pp. 110-122, 2007
VI. A. Rao, M.Garg and S.L.Kalla, “Caputo-type fractional derivative of a
hypergeomatric integral operator” , In Kuwait J. Sci. Eng., Vol.37.1A, pp.
15-29, 2010
VII. D.M. Lee, A.K. Rathie, R.K. Parmar and Y.S. Kim, “Generalization of
extended beta function, hypergeomatric and confluent hypergeomatric
functions”, Honam math.J., Vol.33, Issue 2, pp. 187-196, 2011
VIII. E.D.Raiville, “Special functions” Macmillan Company, New York. 1960;
Reprinted by Chelsea Publishing Company, Bronx, New York, 1971

IX. E. ̈ zergin, “Some properties of hypergeometric functions”, Ph.D Thesis,
Eastern Mediterranean University, North Cyprus, February 2011
X. E. ̈ zergin, M.A. ̈ zarslan and A. Altin, “Extension of gamma, beta and
hypergeometric functions”, J.Comput. Appl. Math., Vol. 235, pp.4601-4616,
2011
XI. E.T.Wittaker and G.N. Watson, “A Course of Modern Analysis, Cambridge”,
Cambridge Univ.Press: 1962
XII. H.M.Srivastava and J.Choi, “Zeta and q-Zeta function and associated series
and integrals”, Elsevier science Publishers, Amsterdam, London and New
York, 2012
XIII. H.M. Srivastava and R.K. Saxena, “Operators of fractional integration and
their applications”, Appl. Math. Comput. Vol. 118,pp.1-52, 2001
XIV. H.M. Srivastava, R.K. Parmar and P.Chopra, “A class of extended fractional
derivative operators and associated generating relations involving
hypergeometric functions”, Axioms I, pp.238-258, 2012
XV. I.N. Sneddon, “The Use of Integral Transforms”, Tata McGraw Hill, New
Delhi, 1979
XVI. J.Choi, P. Agarwal and S.Jain, “Certain fractional integral operators and
extended generalized Gauss hypergeomatric functions”, KYUNPOOK
Math.J.,Vol.55, pp. 695-703, 2015
XVII. K.K. Katraia, P. Vellaisamy, “The generalized k-Wright function and
Marichev-Saigo-Maeda fractional operators” (17 Aug 2014),
arXiv:1408.4762v1 [Math.CA]
XVIII. K.S. Nisar, A.F.Eata , M.A.Dhaifallah and J.Choi, “Fractional calculus of
generalized k-Mittag-Leffler function and its applications to statistical
distribution Advance in Difference Equation”, DoI: 10.1186/s 13662-016-
1029-6, pp.1-17, 2016
XIX. K.S.Miller, B.Ross, “An introduction to the fractional calculus and fractional
differential equations”, Wiley, New York 1993
XX. K.S.Nisar and S.R.Mondal, “Marichev-Saigo-Maeda fractional integral
operator and generalized Mittag-Leffer function”, Thai J.Math. 2016
XXI. M.A.Chaudhry, A.Qadir, M. Rafique and S.M. Zubair, “Extension of Euler’s
beta function”, J. Comput.Appl. Math., Vol. 78, pp. 19-32, 1997
XXII. M.A.Chaudhry ,A.Qadir , H.M.Srivastava and R.B.Paris, “Extended
hypergeomatric and confluent hypergeomatric function” , Appl.
Math.Comput, Vol. 159, pp. 589-602, 2004

XXIII. M.Saigo and N.Maeda, “More generalization of fractional calculus, in
transform methods and special function”, Vol. 96, 1998. Bulgarian Academy
of Sciences, Bulgaria, pp. 386-400
XXIV. O.L. Marichev, “Volterra equation of mellin convolution type with a horn
function in the kernel”, Izvestiya Akademii Nauk BSSR, Seriya Fiziko-
Mathematicheskikh Nauk, Vol.1,pp.128-129, 1974
XXV. P. Agarwal, G.V. Milovanovic and K.S. Nisar, “A fractional integral
operators involving the Mittag-Leffler type function with four parameters”,
FACTA UNIVERSITATITS, Ser.Math. Inform. Vol. 30, Issue5, pp. 597-
605, 2015
XXVI. R.K. Saxena and M.Saigo, “Generalized fractional calculus of the H-function
associated with the Appell function F3”, J. Fract. Calc., Vol.19, pp. 89-104,
2001
XXVII. S.R. Mondal and K.S. Nisar, “Marichev-Saigo-Maeda fractional integration
operators and generalized Bessel functions”, Math, Probl. Eng.2014, Article
ID 274093, 1996
XXVIII. S.S. Nair, “Pathway fractional Integration operators”, Fractional calculus and
Applied Analysis, Vol. 12, Issue 3, pp. 237-25, 2009
XXIX. V.Kiryakova, “All the special functions are fractional differintergrals of
elementary functions”, J. Phys. A, Math .Gen. Vol. 14, Issue 30, pp. 5085-
5103, 1997

View Download

Abstract:

Local binary patterns (LBP) is a very powerful texture feature of an image. Many variants of LBP models are available and almost all of the derived models are based on the idea to calculate the difference of each central pixel in the 3×3 neighborhood matrix. Based on this difference is positive or negative, we replace neighborhood pixel intensity with 1 or 0 respectively and then convert obtained 0 and 1 pattern into a decimal value. In this paper, we propose modification of this idea, instead of using local binary pattern, local octal and local hexadecimal pattern is used. Local octal pattern (LOP) and the local hexadecimal pattern(LHP) is further tested on two different datasets of 100 images each of sizes 150 x 150 and the obtained results are compared with the state-of-art local binary pattern. For similarity measure, Euclidian distance and Manhattan distance is used. Results show that local octal pattern is superior over local hexadecimal pattern and the local binary pattern is superior over both local octal pattern and local hexadecimal pattern.

Keywords:

Feature extraction,local binary pattern,texture feature,content based image retrieval,pixel,pixel intensity,

Refference:

I. A. Alaknanda, A. Nitin: ‘Content based image retrieval using Histogram and
LBP’, International Journal of Communication System and Network
Technology, vol. 5, No. 1, 2016, pp. 50-65
II. B. Zhang, Y. Gao, S. Zhao, J. Liu, “Local derivative pattern versus local
binary pattern: face recognition with high-order local pattern
descriptor”, IEEE Trans. Image Process., vol. 19, pp. 533-544, 2010.
III. He Yonggang, Nong Sang, Changxin Gao, “Pyramid-Based Multi-structure
Local Binary Pattern for Texture Classification” , Pattern Analysis and
Applications 16(4):133-144, November 2010.
IV. Jian Li, Hanyi Du, Yingru Liu , Kai Zhang , Hui Zhou, “Extended
Gradient Local Ternary Pattern for Vehicle Detection” IEEE 17th
International Conference on Computational Science and Engineering, pp.
1882-1885, January 2015.
V. J. Ren, X. Jiang, J. Yuan, “Relaxed local ternary pattern for face
recognition”, IEEE International Conference Image Processing (ICIP),
pp. 2-6, September, 2013.
VI. Jing Yi Tou; Yong Haur Tay; Phooi Yee Lau; “One-dimensional Grey-level
Co-occurrence Matrices for texture classification,” Information Technology,
2008. ITSim 2008, vol.3, no., pp.1-6, 26-28 Aug. 2008.
VII. N. Arora, A. Ashok, S. Tiwari, “Modified Local Binary Pattern Scheme using
Row, Column and Diagonally aligned Pixel’s Intensity Pattern” International
Journal of Innovative Technology and Exploring Engineering (IJITEE), vol.
8, no. 5, pp. 771-779, March 2019.
VIII. Ojala, T., Pietikainen, M., Harwood, D.: ‘A comparative study of texture
measures withclassification based on feature distributions’, Pattern
Recognition, 1996, 29, pp. 51–59
IX. Ojala, T., Pietikainen, M., & Maenpaa, T. (2002). Multiresolution gray-scale
and rotation invariant texture classification with local binary patterns. IEEE
Transactions on Pattern Analysis and Machine Intelligence, 24(7), 971–987.
X. Qiuyan Lin, Jiaying Liu, Zongming Guo, “Local ternary pattern based on
path integral for steganalysis”, 2016 IEEE International Conference on
Image Processing (ICIP), pp. 2737-2741 August 2016.
XI. S. Murala, R.P. Maheshwari, R. Balasubramanian, “Local tetra patterns: a
new feature descriptor for content-based image retrieval”, Trans. Image
Process., vol. 21, no. 5, pp. 2874-2886, 2012.

XII. Sima Soltanpour , Q. M. Jonathan Wu, “Multiscale depth local derivative
pattern for sparse representation based 3D face recognition”, IEEE
International Conference on Systems, Man, and Cybernetics (SMC),
pp. 560-565, December 2017.
XIII. S. R. Dubey, S. Singh, and R. Singh, “Local bit-plane decoded pattern: A
novel feature descriptor for biomedical image retrieval,” IEEE J. Biomed.
Health Informat, vol. 20, no. 4, pp. 1139–1147, Jul. 2016.
XIV. Ying Liu, Dengsheng Zhang, et al.: ‘A survey of Content Based Image
Retrieval with high-level semantics, Pattern Recognition, 40(1):262–282,
January 2007.
XV. X. H. Han, G. Xu, Y. W. Chen, “Robust local ternary patterns for texture
categorization”, 2013 6th International Conference on Biomedical
Engineering and Informatics, pp. 846-850, 2013
XVI. X. Y. Bian, C. Chen, Q. Du, and Y. X. Sheng, “Extended multistructure local
binary pattern for high-resolution image scene classification,” in Proc. IEEE
36th Int. Conf. Geosci. Remote Sens. Symp., 2016, pp. 5134–5137.
XVII. Z. Wang, R. Huang, W. Yang, C. Sun, “An enhanced local ternary patterns
method for face recognition”, Proceedings of the 33rd Chinese Control
Conference, pp. 4636-4640, July 2014

View Download

Abstract:

It is a known fact the power crisis has literally crippled many nations and slowed them down from keeping up with the technological reforms in every field in order to solve he power issue, different renewable energy system are being analyzed and implemented that can be contributed to the power shortage. Since most of the industrial and residential electrical equipment using AC power to operate, these renewable energy systems must have a converter to transform DC power to AC power in attempt of doing, the system is subjected to high frequency harmonics due to converters, which can be degrade system performance. This research intends to find out an effective solution to reduce the high frequency harmonics by designing and implementing filters in solar cell driven AC loads.

Keywords:

Harmonics,AC loads,Filters,Frequency,Renewable Energy,Solar PV,

Refference:

I. A. H. Alami, Effects of evaporative cooling on efficiency of
photovoltaic modules, Energy conversion and management, vol. 77.
pp. 668-679, (2014).
III. G.K. singh, Solar power generation by Pvtechnology: A
review”Energy,vol.53,1-13,(2013).
IV. G.N Tiwari,R.K. Mishra,S.C. Solanki”Potovoltivc modules ant their
appllications: A review on thermal modelling “ applied energy 88(7),2287-
2304,(2011).
IV. H. Bahaidarah , A. Subhan , P. Gandhidasan, S. Rehman,
Performance evaluation of a PV module by back surface water
cooling for hot climate conditions, Energy 59,445-453, (2013).
V. H. G. Teo, P. S. Lee, M. N. A. Hawlaser, An active cooling
systemfor photovoltaic modules, Applied Energy 90(1), 309-315,
(2012).
VI. Mirzae, P. A., Zhang, R., Validation of a climatic CFD model to
predict the surface temperature of building integrated photovoltaics,
Energy procedia 78(2018) 1865-1870.
VIII *1Muhammad AamirAman, 2Muhammad ZulqarnainAbbasi, 3Hamza
Umar Afridi, 4Khushal Muhammad, 5Mehr-e-Munir Prevailing Pakistan’s
Energy Crises.1,2,3,4,5 Department of Electrical Engineering, Iqra National
University, Pakistan Email: aamiraman@inu.edu.pk *Corresponding
author: Muhammad AamirAman, E-mail:
aamiraman@inu.edu.pkJ.Mech.Cont.& Math. Sci., Vol.-13, No.-4,
September-October (2018) Pages 147-154
IX *1 Muhammad AamirAman, 2Muhammad ZulqarnainAbbasi, 3Hamza
Umar Afridi, 4Mehr-e-Munir, 5 Jehanzeb Khan. Photovoltaic (PV) System
Feasibility for UrmarPayan a Rural Cell Sites in Pakistan Department of
Electrical Engineering, Iqra National University, Pakistan. Email:
aamiraman@inu.edu.pk *Corresponding author: Muhammad AamirAman,
E-mail: aamiraman@inu.edu.pkJ.Mech.Cont.& Math. Sci., Vol.-13, No.-3,
July-August (2018) Pages 173-179
X *1Muhammad AamirAman, 2Muhammad ZulqarnainAbbasi, 3Murad Ali,
4Akhtar Khan.To Negate the influences of Un-deterministic Dispersed
Generation on Interconnection to the Distributed System considering Power
Losses of the system 1 Department of Electrical Engineering, Iqra National
University, Pakistan Email : aamiraman@inu.edu.pk *Corresponding
author: Muhammad AamirAman, E-mail:
aamiraman@inu.edu.pkJ.Mech.Cont.& Math. Sci., Vol.-13, No.-3, July-
August (2018) Pages 117-132
XI Nizetic, S., Grubisic-Cabo, F., Marinic-Kragic, I., Papadopulos, A. M.,
Experimental and numerical investigation of a backside convective cooling
mechanism on photovoltaic panel, Energy, vol. 111, 211-225, (2016).

View Download

Abstract:

Telecommunication quality of service and customer satisfaction are the importantdecisive factors responsible for shifting of loyalties and increase profitability to the face the fierce competition in Pakistan telecommunication market comprised of 154 million cellular subscribers with 73.85% Teledensity. This paper intend to determine relationship among these variables and their impact on customer switching to another operator which has also become global phenomena. The analysis is conducted on primary data collected that is randomly sampled. The results clearly indicate the strong positive relations of value added services on service quality & customer satisfaction and strongly negative relationship with customer propensity to churn in Pakistan Telecom Environment. Resultantly, the customer churn can easily be controlled by providing enhance quality of voice, robust and reliable connectivity, better complaint management, customer care, and value added services with adequate features.

Keywords:

Service quality,Customer Satisfaction,Customer Churn,Customer Loyalty,

Refference:

I. Abbas Al-Refaie, Murad Al-Tarawneh, Nour Bata “ study of customer
churn in the telecom industry using structural equation modelling”
Journal of International Scientific Publications Economy & Business
ISSN 1314-7242, Volume 12, 2018
II. Abbas Al-Refaie “Cluster Analysis of Customer Churn in Telecom
Industry”, World Academy of Science, Engineering and Technology
International Journal of Social, Behavioral, Educational, Economic,
Business and Industrial Engineering Vol:11, No:5, 2017.
III. Adnan, Feras, Babar, Awais, sajjad “Customer churn prediction in
telecommunication industry using data certainty”, Journal of Business
Research Volume 94, January 2019, Page 290-301

IV. Alrend, Anju, Indu, Jay, Erbeth, Leslyn,”Determining the intervening
effects of exploratory data analysis and feature engineering in telecoms
customer churn modelling”,2019 4th MEC International Conference on
Big data and Smart city (ICBDSC).
V. Alrence Santiago Halibas ; Anju Cherian Matthew ; InduGovinda
Pillai ; Jay Harold Reazol ; Erbeth Gerald De “Determining the
Intervening Effects of Exploratory Data Analysis and Feature Engineering
in Telecoms Customer Churn Modelling” 2019 4th MEC International
Conference on Big Data and Smart City (ICBDSC)
VI. Amin A., Shehzad S., Khan C., Ali I., Anwar S. (2015) Churn
Prediction in Telecommunication Industry Using Rough Set Approach.
In: Camacho D., Kim SW., Trawiński B. (eds) New Trends in
Computational Collective Intelligence. Studies in Computational
Intelligence, vol 572 pp 83-95
VII. Amit C and InduUprety, “Analysis of telecom service quality factors with
analytic hierarchy process and fuzzy extent analysis: a case of public
sector unit”, Int. J. Business and Systems Research, Vol. 10, Nos. 2/3/4,
2016
VIII. Archi D , Dr. A.K. Srivastava, Impact of Service Quality on Customer
Loyalty- A Study on Telecom Sector in India”, IOSR Journal of Business
and Management (IOSR-JBM) Volume 18, Issue 2 .Ver. I (Feb. 2016), PP
45-55
IX. A.Saran Kumar, Dr. D. Chandrakala “An Optimal Churn Prediction
Model using Support Vector Machine with Adaboost” International
Journal of Scientific Research in Computer Science, Engineering and
Information Technology © 2017 IJSRCSEIT | Volume 2 | Issue 1
X. W.B. Huang, M. T. Kechadi, and B. Buckley, “Customer churn prediction
in telecommunications,” Expert Syst. Appl., vol. 39,no. 1, pp. 1414–1425,
Jan. 2012.
XI. Choi, S.-K.,Lee,M.H.,&Chung,G. H.(2001).Competition in Korean mobile
telecommunications market: Business strategy and regulatory
environment. Telecommunications Policy, 25, 125–138.
XII. Dirk Van den Poel, Bart Larivière, and “Customer Attrition Analysis for
Financial Services Using Proportional Hazard Models” issue 13(3), 45-55
(2017) [2]
XIII. Fornell, C. (1992). A national customer satisfaction barometer: The
Swedish experience. Journal of Marketing, 56, 6–21.
XIV. Greenwell, T.C., Fink, J.S., Pastore, D.L., 2002. Assessing the influence
of the physical sports facility on customer satisfaction within the context
of the service experience. Sport Manag. Rev. 5 (2), 129–148[28]
XV. Green, D. (1994), What is Quality in Higher Education?, 1st ed., The
Society for Research into Higher Education & Open University press,
Buckingham.
XVI. Jae-Hyeon, , Sang-Pil, Yung “Customer churn analysis: Churn determinants and mediation effects of partial defection in the Korean mobile telecommunications service industry” Telecommunications Policy 30 (2006) 552–568

XVII. Jayawardhana, P., Perera, D., Kumara, A. and Paranawithana, A
“Kanthaka: Big Data Caller Detail Record (CDR) Analyzer for Near Real
Time Telecom Promotions.” In 2013 4th International Conference on
Intelligent Systems, Modelling and Simulation, pp. 534-538.IEEE, (2013).
XVIII. J. Hadden, A. Tiwari, R. Roy, and D. Ruta, “Computer assisted customer
churn management: State-of-the-art and futuretrends,” Comput. Oper.
Res., vol. 34, no. 10, pp. 2902–2917, Oct. 2007.
XIX. Jay KandampullyTingting (Christina) Zhang Anil Bilgihan ,
(2015),”Customer loyalty: a review and future directions with a special
focus on the hospitality industry”, International Journal of Contemporary
Hospitality Management, Vol. 27 Iss 3 pp. 379 – 414
XX. Kim, Hee-Su., & Kwon, N. (2003). The advantage of network size in
acquiring new subscribers. Information Economics and Policy, 15(1), 17–
33
XXI. Kiran Dahiya, Surbhi Bhatia” Customer churn analysis in telecom
industry” 2015, 4th International Conference on Reliability, Infocom
Technologies and Optimization (ICRITO) (Trends and Future Directions.
XXII. Kotler, P., & Keller, K. L. (2009). Marketing management (13th ed.). New
Jersey: Pearson Prentice Hall.
XXIII. Mahafuz M, Md.Fazl, Nusrat C, Priodorshine S, “Customer satisfaction,
switching intentions, perceived switching costs, and perceived alternative
attractiveness in Bangladesh mobile telecommunications market”, South
Asian Journal of Business Studies 2017, Vol. 6 Issue: 2
XXIV. Manish, Awadhesh, AP Rathore,”Prediction Model for Telecom Postpaid
Customer Churn using Six-Sigma Methodology”, International Journal of
Manufacturing Technology and Management , Volume 31, Issue 5, 2017
XXV. Muhammad Azeem , Muhammad Usman “A fuzzy based churn prediction
and retention model for prepaid customers in telecom industry”
International Journal of Computational Intelligence Systems, Vol. 11
(2018) 66–78
XXVI. Oliver, R. L. (1997). Satisfaction: A behavioral perspective on the
consumer. Boston: Irwin McGraw-Hill
XXVII. PiasonViriri , Maxwell Phiri, “Determinants of Customer Satisfaction in
Zimbabwe Telecommunication Industry” , J Communication, 8(1): 101-
104 (2017)
XXVIII.Ritter, T., &Gemünden, H.G., (2003). “Network competence: Its impact on
innovation success and its antecedents”. Journal of Business Research,
56(9), 745-755.
XXIX. Roberts,J.H, ”Developing new rules for new markets”, Journal of the
Academy of Marketing Science, issue 28(1), 31–44 (2000).
XXX. Sebastiaan,Eugen, Bart, vanden,“Profit driven decision trees for churn
prediction” European Journal of Operational Research,2018
XXXI. Shreya M, Shubham G, Shubhangi J, Vidushi M, Artika S “Survey on
Prediction of Customer Churn Analysis in a Telecommunications Industry”, International Journal for Research in Engineering Application & Management (IJREAM)Vol-02, Issue 07, Oct 2016
XXXII. Spath, 2009, Introduction to Healthcare Quality Management,1st Edition.
Health Administration Press, Chicago, Illinois.
XXXIII.TVafeidadis, K.I. Diamantaras, Sarigiannidis, Chatzisavvas“ A
comparison of machine learning techniques for customer churn
prediction”, Simulation Modelling Practice and Theory Volume55,
June 2015 Pages1-9
XXXIV.Wenjie B, Meili C, Guo Li” A Big Data Clustering Algorithm for Mitigating
the Risk of Customer Churn” IEEE TRANSACTIONS ON INDUSTRIAL
INFORMATICS, VOL. 12, NO. 3, JUNE 2016
XXXV. Yiging H, Fangzhou Z, Mingxuan Y, KeYanhua Li, Bing N, Jia Z” Telco
Churn Prediction with Big Data”proceedings of the 2015 ACM Sigmod
International conference on management of data page 607-618, 2015
XXXVI. Zeithaml, Valerie A. &Bitner, Mary J. (2000) Services Marketing:
Integrating customer focus across the firm, 2nd ed., Irwin/ McGraw-Hill,
Boston, M.A

 

 

View Download

Abstract:

The Optical Network is considered an important asset to any telecom operator. One of the most critical issues to the operators is how they can minimize the deployment cost and maximize the Return of Investments (ROI) by optimizing the operational costs in the optical network. Deployment of future-proof access networks requires new infrastructure and new equipment and, on top of it, raises many questions regarding the costs and risks associated with the technology, telecommunications market, and legal regulations of these networks. This paper presents the techno-economic analysis of the planning of FTTH access network topologies with GPON technologies that includes a series of scenarios in combination with tree, eye and tree topologies of eye and architectures Home-Run and GPON. In order to get realistic results, the techno-economic study has been applied to different urban areas in the city of Peshawar, capital of KPK. Cost/benefit analysis is performed in order to determine the most influential parameters and give general guidelines for the deployment of new-generation optical access networks in different environments. Analysis also shows that the price for new services that a customer needs to pay is competitive in the market today. Today, the service providers seek penetrate the telecommunications market with more advanced plans and complex network designs to reach a greater number of users and expand the range of services that offer. This is where FTTH networks along with technology GPON play an important role, as they meet this challenge. In this work, we present a FTTH network with GPON technology, the parameters related to the main conduit and network Elements (NE) connected to the Splice points (SP), among other aspects. Combining these topologies with their respective architectures would help the network planners to reduce the planning time of this type of networks and investment costs.

Keywords:

Fiber to the Home,Access Network Topologies,Home-Run and Gigabit Passive Optical Network architectures,

Refference:

I. A. Mitcsenkov, P. Katzenberger, P. Bakos, G. Paksy. Automatic map-based
FTTx access network design, 22nd European Regional Conference of the
International Telecommunications Society (ITS2011), Budapest, 18-21:
Innovative ICT Applications Emerging Regulatory, Economic and Policy
Issues, 2011.
II. Amable, Guillermo Zeballos, and Jaime Felipe Vela. “Design topologies
optimization in GPON networks based on population densities using k-means
clustering algorithm.” In 2019 International Conference on Electronics,
Communications and Computers (CONIELECOMP), pp. 60-65. IEEE, 2019.
III. Bajunaid, Noor, and Daniel A. Menascé. “Efficient modeling and optimizing
of check pointing in concurrent component-based software systems.” Journal
of Systems and Software139 (2018): 1-13.
IV. Comm: Scape Solutions Marketing. (October, 2013). GPON – EPON
Comparison. White Paper, (Consult ado el: 10/04/2014), Disponibleen:
http://www.commscope.com/…Comparison_WP_ 107285.pdf
V. D. Jasim and N. Abdul-Rahman. Design and Implementation of a Practical
FTTH Network. International Journal of Computer Applications, Volume 72,
No.12, 2013.
VI. Fritzsche, Lutz, Mathias Schweigel, and Rong Zhao. “Integrated Network
Planning: A Key Success Factor for Network Operators.” In Future Telco,
pp. 43-52. Springer, Cham, 2019.
VII. Garg, Sukriti, and Abhishek Dixit. “Models for Evaluating Power Saving
Techniques in Flexible Optical Access Networks.” In 2018 20th International
Conference on Transparent Optical Networks (ICTON), pp. 1-4. IEEE, 2018.
VIII. J. Segarra, V. Sales and J. Prat. Access Services Availability and Traffic
Forecast in PON Deployment in Proc. ICTON 2011, Stockholm, Sweden.
IX. J. Segarra, V. Sales and J. Prat. Planning and Designing FTTH Networks:
Elements, Tools and Practical Issues, in Proc. ICTON 2012, Stockholm,
Sweden.
X. K. Wieland. The FTTx Mini-Guide. White Paper, Ed. Nexans and Telecomm
– Magazine, 2007.
XI. Kaur, Randeep, and NitikaSoni. “Passive optical network Ns: A Review.”
In 6th Int. Conf. on Recent Trends in Engi., Sci. & Manage., India, pp. 182-
189. 2017.
XII. M. Mahmoud. Design and Implementation of a Fiber to the home FTTH
Access Network based on GPON. International Journal of Computer
Applications, Volume 92-No.6, 2014.
XIII. M. Tahir Riaz, Gustav H. Haraldsson, Jose G. Lopaz, Jens M. Pedersen,
Ole B. Madsen. On Planning of FTTH Access Networks with and without
Redundancy. Jacques Berleur; Magda David Hercheui; Lorenz M. Hilty.
What Kind of Information Society? Governance, Virtually, Surveillance,
Sustainability, Resilience, 328, Springer, pp.326-337, 2010, IFIP Advances in
Information and Communication Technology, 978-3-64215478-
2.<10.1007/978-3-642-15479-931>.<hal-01054784>

XIV. Ma, Yiran, and ZhenshengJia. “Evolution and trends of broadband access
technologies and fiber-wireless systems.” In Fiber-Wireless Convergence in
Next-Generation Communication Networks, pp. 43-75. Springer, Cham,
2017.
XV. Mata, Javier, Ignacio De Miguel, Ramon J. Duran, Noemi Merayo, Sandeep
Kumar Singh, AdmelaJukan, and MohitChamania. “Artificial intelligence
(AI) methods in optical networks: A comprehensive survey.” Optical
switching and networking 28 (2018): 43-57.
XVI. O. Anis, P. Kin, and Ch. Andrej. FTTH Network Design under Power
Budget Constraints. IFIP/IEEE International Symposium on Integrated
Network Management (IM), 2013
XVII. P. Brereton, B. Kitchenham A. Budgen , D. Turner, M., & M. Khalil. Alves,
Carina, Joyce Aline Pereira de Oliveira, and Slinger Jansen. “Software
Ecosystems Governance-A Systematic Literature Review and Research
Agenda.” In ICEIS (3), pp. 215-226. 2017.
XVIII. Ruchet, Bernard, Mario L’heureux, Daniel Gariepy, and Etienne Morindrouin.
“Optical power measurement in a passive optical network.” U.S.
Patent Application 15/822,339, filed April 23, 2019.
XIX. Singh, Puja, and Shashi Prakash. “Optical network unit placement in Fiber-
Wireless (FiWi) access network by Moth-Flame optimization
algorithm.” Optical Fiber Technology 36 (2017): 403-411.
XX. Suman preet and Sanjeev D. Sanjeev. A review on Gigabit Passive
Optical Network (GPON). International Journal of Advanced Research
in Computer and Communication Engineering, Vol. 3, Issue 3, 2014.

View Download

Abstract:

Cloud computing is new style of technology the demand of end user increase day by day it cases more energy consumption.Energy consumption directly connected with the utilization of resource .Batter resource management reduce energy system in the network for that reason in this paper BATalgorithm implement for load balancing technique with different parameter it result compare with ABC algorithm. By implementing BAT algorithm in VM policy it reduces 3% of energy consumption in the network. This result can be achieved by implementing proper load balancing technique due to that it can reduce energy management system in cloud computing.

Keywords:

Cloud computing,Energy Management System,Virtualmachine,loadbalancing,Energy Consumption,

Refference:

I. Almasi, S., &Pratx, G. (2019). Cloud computing for big data. Big Data in
Radiation Oncology
II. Aldakheel, E. A. (2011). A cloud computing framework for computer science
education (Doctoral dissertation, Bowling Green State University)
III. Beloglazov, A., Abawajy, J., & Buyya, R. (2012). Energy-aware resource
allocation heuristics for efficient management of data centers for cloud
computing. Future generation computer systems, 28(5), 755-768
IV. Buyya, R., Yeo, C. S., Venugopal, S., Broberg, J., &Brandic, I. (2009). Cloud
computing and emerging IT platforms: Vision, hype, and reality for delivering
computing as the 5th utility. Future Generation computer systems, 25(6), 599-
616.
V. Buyya, R., Yeo, C. S., Venugopal, S., Broberg, J., &Brandic, I. (2009). Cloud
computing and emerging IT platforms: Vision, hype, and reality for delivering
computing as the 5th utility. Future Generation computer systems, 25(6), 599-
616.

VI. Tso, F. P., White, D. R., Jouet, S., Singer, J., &Pezaros, D. P. (2013, July). The
glasgow raspberry pi cloud: A scale model for cloud computing infrastructures.
In 2013 IEEE 33rd International Conference on Distributed Computing Systems
Workshops (pp. 108-112). IEEE.
VII. Erker, S., Lichtenwoehrer, P., Zach, F., &Stoeglehner, G. (2019).
Interdisciplinary decision support model for grid-bound heat supply systems in
urban areas. Energy, Sustainability and Society, 9(1), 11.
VIII. Buyya, R., Yeo, C. S., Venugopal, S., Broberg, J., &Brandic, I. (2009). Cloud
computing and emerging IT platforms: Vision, hype, and reality for delivering
computing as the 5th utility. Future Generation computer systems, 25(6), 599-
616.
IX. Eager, D. L., Lazowska, E. D., &Zahorjan, J. (1986). Adaptive load sharing in
homogeneous distributed systems. IEEE transactions on software engineering,
(5), 662-675.
X. Ghomi, E. J., Rahmani, A. M., &Qader, N. N. (2017). Load-balancing algorithms
in cloud computing: a survey. Journal of Network and Computer
Applications, 88, 50-71.
XI. Gupta, P., Seetharaman, A., & Raj, J. R. (2013). The usage and adoption of cloud
computing by small and medium businesses. International Journal of Information
Management, 33(5), 861-874
XII. Lawrence, W. J., & Chang, M. J. K. (2018). cloud computing and Virtualization:
the “Entrepreneur without Borders” Workbench for 21st century Enterprise
development. GSTF Journal on Computing (JoC), 1(1).
XIII. Mushtaq, M. F., Akram, U., Khan, I., Khan, S. N., Shahzad, A., & Ullah, A.
(2017). Cloud computing environment and security challenges: A
review. International Journal of Advanced Computer Science and
Application, 8(10), 183-195.
XIV. Mell, P., &Grance, T. (2011). The NIST definition of cloud computing.
XV. Ronchi, A. M. (2019). Interaction Design Essentials. In e-Citizens (pp. 125-156).
Springer, Cham.
XVI. Ullah, A., Nawi, N. M., Shahzad, A., Khan, S. N., &Aamir, M. (2017). An Elearning
System in Malaysia based on Green Computing and Energy
Level. JOIV: International Journal on Informatics Visualization, 1(4-2), 184-187.
XVII. Umar, S., & Baseer, S. (2016, August). Perception of cloud computing in
universities of Peshawar, Pakistan. In 2016 Sixth International Conference on
Innovative Computing Technology (INTECH) (pp. 87-91). IEEE.

XVIII. Subashini, S., &Kavitha, V. (2011A). A survey on security issues in service
delivery models of cloud computing. Journal of network and computer
applications, 34(1), 1-11.
XIX. Subashini, S., &Kavitha, V. (2011B). A survey on security issues in service
delivery models of cloud computing. Journal of network and computer
applications, 34(1), 1-11.

View Download

Abstract:

Nowadays, artificial intelligence systems become actively used for the identification of different diseases using their medical data. Most of existing traditional medical systems are based on the knowledge of experts-doctors. In this thesis, the application of soft computing elements is considered to automate the process of diagnosing diseases, in particularly diagnosing of a heart attack. The research work will offer probable help to the medical practitioners and healthcare sector in making instantaneous resolution during the diagnosis of the diseases. The intelligent system will predict heart attacks from the patient dataset utilizing algorithms and help doctors in making diagnose of these illnesses. In this study, three techniques such as a neural network (back propagation), Fuzzy Inference System (FIS) and Adaptative Neuro-Fuzzy System (ANFIS) are considered for the design of the prediction system. The systems are designed using data sets. The data sets contain 1319 samples that includes 8 input attributes and one output. The output refers presence of a heart attack in the patient. For comparative analysis, the simulation results of the ANFIS model is compared with the simulation results of the neural network-based prediction model. The ANFIS model has shown better performance and outperformed NN based model. The obtained simulation results demonstrate the efficiency of using ANFIS model in the identification of heart attacks.

Keywords:

Artificial neural network,adaptive neuro-fuzzy inference system,fuzzy inference System (FIS),neural network (back propagation),heart attack,

Refference:

I. Adeli, A., &Neshat, M. (2010, March). A fuzzy expert system for heart disease
diagnosis. In Proceedings of International Multi Conference of Engineers and
Computer Scientists, Hong Kong (Vol. 1).
II. American Heart Association (2015). Heart.org/answers by heartNational
Center7272.Greenville.Ave.Dallas, TX 75231Customer Service
1-800-AHA-USA-11-800-242-8721 from https://www.heart.org/en/healthtopics/
consumer-healthcare/answers-by-heart-fact-sheets/answers-by-heart-factsheets-
lifestyle-and-risk-reduction /
III. Feshki, M. G., &Shijani, O. S. (2016, April). Improving the heart disease
diagnosis by evolutionary algorithm of PSO and Feed Forward Neural Network.
In Artificial Intelligence and Robotics (IRANOPEN), 2016 (pp. 48-53). IEEE.
IV. Haykin S (2009) Neural network and machine learning.3rd edCopyright © 2009
by Pearson Education, Inc., Upper Saddle River, New Jersey 07458. Pearson
Prentice Hall, New York ISBN-13: 978-0-13-147139-9 ISBN-10: 0-13-147139-2.
V. Teng, H., Liu, X., Liu, A., Shen, H., Huang, C., & Wang, T. (2018). Adaptive
transmission power control for reliable data forwarding in sensor based
networks. Wireless Communications and Mobile Computing, 2018.
VI. Narcisse, M. R., Rowland, B., Long, C. R., Felix, H., &McElfish, P. A. (2019).
Heart Attack and Stroke Symptoms Knowledge of Native Hawaiians and Pacific
Islanders in the United States: Findings From the National Health Interview
Survey. Health promotion practice, 1524839919845669.

VII. Takdastan, A., Mirzabeygi, M., Yousefi, M., Abbasnia, A., Khodadadia, R.,
Soleimani, H., …&Naghan, D. J. (2018). Neuro-fuzzy inference system
Prediction of stability indices and Sodium absorption ratio in Lordegan rural
drinking water resources in west Iran. Data in brief, 18, 255-261.
VIII. Nicole, O., Bell, D. M., Leste-Lasserre, T., Doat, H., Guillemot, F., &Pacary, E.
(2018). CaMKIIβ regulates nucleus-centrosome coupling in locomoting neurons
of the developing cerebral cortex. Molecular psychiatry, 23(11), 2111.
IX. Jang, J. S. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE
transactions on systems, man, and cybernetics, 23(3), 665-685.
X. Kaya, E., Oran, B., &Arslan, A. (2011). A diagnostic fuzzy rule-based system for
congenital heart disease. World academy of science, Engineering and
technology, 78, 253-256.
XI. Patil, S. B., &Kumaraswamy, Y. S. (2009). Intelligent and effective heart attack
prediction system using data mining and artificial neural network. European
Journal of Scientific Research, 31(4), 642-656.
XII. Shanthi. S (February 2017). Customized Prediction of Heart Disease with
Adaptive Neuro Fuzzy Inference System International Journal of Advanced
Research in Computer and Communication Engineering, SO 3297:2007 Certified
XIII. Shinde, P. P., Oza, K. S., &Kamat, R. K (2016). An Analysis of Data Mining
Techniques in Aggregation with Real Time Dataset for the Prediction of Heart
Disease. InternationalJournal of Control Theory and Applications, 9(20), 327-336.
XIV. Sundar, N. A., Latha, P. P., & Chandra, M. R. (2012). Performance analysis of
classification data mining techniques over heart disease database. IJESAT]
International Journal of engineering science & advanced technology ISSN,
2250-3676.
XV. Vipul, A. S. (2009). Adaptive Neuro-Fuzzy Inference System for Effect of Wall
Capacitance in a Batch Reactor. Advances in Fuzzy Mathematics ISSN, 69-75.
XVI. WHO (2015). Cardiovascular diseases. Retrieved August 28, 2015 from
http://www.who.int/ media centre/factsheets/fs317/en /.
XVII. Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338-353
XVIII. Zadeh, L. A. (1973). Outline of a new approach to the analysis of complex
systems and decision processes. IEEE Transactions on systems, Man, and
Cybernetics, (1), 28-44.

View Download