Archive

Abstract:

This paper details anM/M/1 system with breakdowns, two varieties of repair facilities, timeout and vacation. As soon as the system is vacant, the server pauses for a period 'c'. If no unit arrives at this time, the server get-away otherwise instigate the service to all the customers who gets in. There will be no delay in repair if the system breaks down as it is aided with two varieties of Repair facilities (TRF) based on the stage of service where it has failed. More clearly repair of Type-1 will be started with a chance of 1-q if the server fails atamidst of service and the other type is started with a likelihood q if failurehappens before the staring of service”. Various constants are derived and also done sensitivity analysis.

Keywords:

Vacation queuing system, two varieties of Repair facilities and timeout,breakdowns, length of the system,

Refference:

I Doshi, B.T (1986), Queuing system with vacations. A survey on queuing system: Theory and Applications. 1(1), 29-66.
II GeniGupur, (2010), Analysis of the M/G/1 retrial Queuing Model with server breakdowns. Oper. 1:313-340.
III H.White and L. Christie. (1958),Queuing with preemptive priorities or with breakdown. Operations System, vol. 6(1), 79-95.
IV K.C. Madan, (2003), An M/G/1 type queue with Time-Homogeneous Breakdowns and Deterministic Repair Times, Soochow Journal of Mathematics Volume 29, No. 1, pp. 103110..
V Levy.Y and Yechiali. U, (1975), Utilization of Idle Time in an M/G/1 Queuing System. Management Science, 22, 202-211. http://dx.doi.org/10.1287/mnsc.22.2.202.
VI Oliver C. Ibe (2007) Analysis and optimization of M/G/1 Vacation Queuing Systems with Server Timeout, Electronic Modeling, V.29, no. 4, ISSN 0204-3572.
VII S.Bama, M.I.Afthab Begum and P.Fijy Jose, (2015), Unreliable MX/G/1 queueing system with two types of Repair. International Journal of Innovative Research & Development, Vol. 4, No. 10, pp. 25-38.
VIII Y.Saritha, V.N. Rama Devi and K.Chandan (2020), M/G/1 Queue with Vacation, Two Cases of Repair Facilities and Server Timeout.TEST Engineering and Management,Vol.82,pp. 16358 – 16363.

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Abstract:

In the underwater sensor network, the accurate position of every sensor node is of prime importance and the procedure of finding the node coordinates is known as localization. Localization plays a vital role in the designing and functioning of any Underwater Sensor Network(UWSN).Cheng et al(III) prove effective localization algorithm has a greater influence on the performance of the network.Recent research exists in the field of exploring meta-heuristic based localizationalgorithms for effective sensor node localization by Kulkarniet al. (XI), and Kumaret al.(XII). The research contributions of  Li& Wang (XIII), Goyal S Patterh& MS (VII) have proved that the cuckoo search(CS) algorithm is comparatively effectivebecause of its distinctiveness of few parameters thus dropping the computational complication and communication overhead.CS has also proved to have better proficient

Keywords:

Sensor,cuckoo,search, underwater,network, node,

Refference:

I. Adnan A and Razzaque MA, “A comparative study of Particle Swarm Optimization and Cuckoo Search techniques through problem-specific distance function”, Proceedings of Information and Communication Technology (ICoICT), vol. 160(1), pp. 83-92, 2013.
II. Arora S and Singh S,“A conceptual comparison of firefly algorithm, bat algorithm and cuckoo search”, Proceedings of International conference on Control Computing Communication and Materials (ICCCCM), pp. 1-4, 2013.
III. Cheng J and Xia L, “An Effective Cuckoo Search Algorithm for Node Localization in Wireless Sensor Network”,Sensors, Vol.16(9), pp.1390-1407, 2016.
IV. Cheng W, Teymorian AY, Ma L, Cheng X, Lu X, and Lu Z, “Underwater Localization in Sparse 3D Acoustic Sensor Networks”, Proceedings of 27th IEEE Conference on Computer Communications, pp. 236-240, 2008.
V. Doherty L,Pister K, and El Ghaoui L, “Convex Position Estimation in Wireless Sensor Networks”, Proceedings of the INFOCOM 2001- Twentieth Annual Joint Conference of the IEEE Computer and Communications Societies, Helsinki, Finland, Volume 3, pp. 1655–1663, 2001.
VI. Gao J, Shen X, Zhao R ,Mei H, and Wang H, “A Double Rate Localization Algorithm with One Anchor for Multi-Hop Underwater Acoustic Networks” , Sensors, Vol.17(5), pp.984-1001, 2017.
VII. Goyal S and Patterh MS, “Wireless sensor network localization based on cuckoo search algorithm”, Journal of Wireless Personal Communication, vol. 79, pp. 223-234, 2014.
VIII. Han G, Jiang J, Shu L, Xu Y, and Wang F, “Localization Algorithms of Underwater Wireless Sensor Networks: A Survey”, Journal of Sensors, pp. 2026-2061, 2012.
IX. Han G, Zhang C, Shu L, and Rodrigues JJPC, “Impacts of Deployment Strategies on Localization Performance in Underwater Acoustic Sensor Networks”, IEEE Transactions on Industrial Electronics, vol. 62(3), pp. 1725-1733, 2015.
X. Harikrishnan R, Kumar VJS, and Ponmalar PS, “Firefly algorithm approach for localization in wireless sensor networks”, Proceedings of 3rd International Conference on Advanced Computing, pp. 209-214, 2016.
XI. Kulkarni RV, Venayagamoorthy GK, and Cheng MX, “Bioinspired node localization in wireless sensor networks”, Proceedings of International Conference on Systems, Man and Cybernetics, IEEE, pp. 205-210, 2009.
XII. Kumar A, Khosla A, Saini JS, and Singh S, “Meta-heuristic range based node localization algorithm for Wireless Sensor Networks”, In Proceedings of the IEEE International Conference on Localization and GNSS, pp. 1-7, 2012.
XIII. Li SP and Wang XH, “The research on Wireless Sensor Network node positioning based on DV-hop algorithm and cuckoo searching algorithm”, Proceedings of the IEEE International Conference on Mechatronic Sciences, Electric Engineering and Computer (MEC), pp. 620-623, 2013.
XIV. Priyadharsini Cand Kannimuthu S, “Polyhedron Model for Three Dimensional Node Deployment in Underwater Sensor Network”, Journal of Computational and Theoretical Nanoscience, vol. 14(12), pp. 5858-5862, 2017.
XV. Solihin MI and Zanil MF, “Performance comparison of cuckoo search and differential evolution algorithm for constrained optimization”, International Engineering Research and Innovation Symposium (IRIS), pp. 1-8, 2016.
XVI. Yang XS and Deb S, “Cuckoo Search via Levy Flights”, Proceedings of World Congress on Nature and Biologically Inspired Computing (NaBIC 2009), pp. 210-214, 2009.

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Abstract:

A series of glasses by melt quenching method fabricated for spectroscopic investigations of Dy³⁺ ions doped Antimony (Sb)-Magnesium (Mg)-Strontium (Sr) Oxyfluoroborate (BSbMgFS) glasses. The structural and optical characterizations such as XRD, Raman, UV-visible-NIR absorption spectroscopy, photoluminescence (PL) (excitation and emission), were skilled to study the various properties of the glasses. Amorphous nature of present glass confirm from the broad peaks of XRD.  The transitions from lowest energy state to excited state in RE³⁺ ions were identified using optical UV-visible-NIR absorption spectra. By using Judd-Ofelt theory the J-O intensity parameters Ω_λ (λ = 2, 4, 6) have been evaluated from experimental (f_exp) and calculated (f_cal) oscillator strengths. The value of Ω₂ is higher than Ω₄ and Ω₆ and follows the trend Ω₂˃ Ω₆˃ Ω₄. This confirms the high covalency of Dy³⁺ ion with ligands and more asymmetric environment around the rare earth ion in host. The emission of light from glass system was concluded through PL spectra (Excitation and emission) for Dy³⁺ion. In the present work branching ratio of ⁴F_9/2→⁶H_13/2transition is obtained higher than 50% (0.55). The highest readings of A_R, β_R and σ_se are obtained for the transition n ⁴F_9/2→⁶H_13/2 (yellow).Hence, this can be consider as an appropriate mechanism for lasing action. Gain band width (Δλ_eff x σ_se)and optical-gain (σ_se x τ_R) were found to be high for BSbMgFSDy01 and this suggest that BSbMgFSD01 glasses were appropriate for optical amplifier. In the present study of Dy³⁺ -doped glasses, BSbMgFSD05 has shown highest emission with a Y/B ratio of 2.73 which is useful for white-LED applications. BSbMgFSDy05 glass is suitable for white light emitting devices and lasers applications in the visible region at 575 nm upon excitation of 425 nm.

Keywords:

Photoluminescence, Judd-Ofelt theory, PL spectra,Dy3+ -doped glasses,

Refference:

I. A. Lira, A. Speghini, E. Camarillo, M. Bettinelli, U. Caldino, Spectroscopic evaluation of Zn (Po3): Dy3+ glass as active medium of solid state laser, Opt. Mater. 38 (2014) 188.

II. A.S. Rao, Y.N. Ahammed, R.R. Reddy, T.V.R. Rao, Spectroscopic studies of Nd3+-doped alkali fluoroborophosphate glasses, Opt. Mater. 10 (1998) 245–252.

III. A. Thulasiramudu, S. Buddhudu, Optical characterization of Sm3+ and Dy3+ doped ZnO-PbO-B2O3 glasses, Spectrochim Acta Part A. 67 (2007) 802-807.

IV. B. R. Judd, Optical absorption intensities of rare earth ions, Phys. Rev. 127 (1962) 750.

V. C. Gorller-Walrand, K. Binnemans, Handbook on the Physics and Chemistry of Rare Earths, Spectral Intensities of f-f Transitions, vol. 5, Elsevier/North-Holand, Amsterdam, 1998, 101-264.

VI. C.K. Jorgenson, B.R. Judd, Hypersensitive pseudoquadrapole transition in Lanthanides, Mol. Phys. 8 (1964) 281–290.

VII. C. Nageswara Raju, S.Sailaja, S. Hemasundara Raju, S.J.Dhoble, U.Rambabu, Young-Dahl Jho, B.Sudhakar Reddy, Emission analysis of CdO–Bi2O3–B2O3 glasses doped with Eu3+ and Tb3+,Ceramic.International 40(2014) 7701–7709.

VIII. D.K. Sardar, W.M. Bradly, R.M. Yow, J.B. Gruber, B. Zandi, J. of Luminescence 106 (2004) 195-203.

IX. D. Rajesh, Y.C. Ratnakaram, M. Seshadri, A. Balakrishna, T. Satya Krishna, Structural and luminescence properties of Dy3+ ion in strontium lithium bismuth borate glasses J. Lumin. 132 (2012) 841-849.

X. G. Chinna Ram, T. Narendrudu, S. Suresh, A. Suneel Kumar, M.V. Sambasiva Rao, V. Ravi Kumar, D. Krishna Rao, Investigation of luminescence and laser transition of Dy3+ion in P2O5-PbO-Bi2O3 -Dy2O3 glasses, Optical Materials 66 (2017) 189-196.

XI. G. S. Ofelt, Intensities of crystal spectra of rare earth ions, J. Chem. Phys. 37 (1962) 511.

XII. G. Venkata Rao, C.K. Jayasankar., “Dy3+-doped tellurite based tungsten zirconium glasses: Spectroscopy study”, J. Mol. Struct. 1084 (2015) 182-189.

XIII. H.A. Othman, G.M. Arzumanyan, D. Moncke, The effect of alkaline earth oxides and cerium concentration on the spectroscopic properties of Sm/Ce doped lithium alumino-phosphate glasses Opt. Mater. 62 (2016) 689–696.

XIV. J. Juarez-Batalla, A.N. Meza-Rocha, G.Munoz, H.I.Camarillo, U.Caldino, Luminescence properties of Tb3+-doped zinc phosphate glasses for green laser application, Opt Mater. 58(2016) 406–411.

XV. Kenyon A.J, “Recent developments in rare-earth doped materials for optoelectronics, Prog. J. Quantum Electron, 26(2002) 225–284.

XVI. K. Jaroszewski, P. Głuchowski, M. Chrunik, R. Jastrz, A. Majchrowski, D. Kasprowicz, Near-infrared luminescence of Bi2ZnOB2O6:Nd3+/PMMA composite, Optical Materials 75 (2018) 13-18.

XVII. K.S.V. Sudhakar, M. Srinivasa Reddy, L. Srinivasa Rao, N. Veeraiah, Influence of modifier oxide on spectroscopic and thermoluminescence characteristics of Sm3+ ion in antimony borate glass system, J. of Luminescence 128 (2008) 1791– 1798.

XVIII. K. Swapna, Sk. Mahamuda, A. Srinivasa Rao, M. Jayasimhadri, T. Sasikala, L. Rama Moorthy, Optical absorption and luminescence characteristics of Dy3+ doped Zinc Alumino Bismuth Borate glasses for lasing materials and white LEDs, Journal of Luminescence 139 (2013) 119 -124.

XIX. K. Vijaya Babu, Sandhya Cole, Luminescence properties of Dy3+-doped alkali lead alumino borosilicate glasses, Ceramics International(2018) 9080-9090.

XX. K.V. Krishnaiah, K. Upendra Kumar, C.K. Jayasankar, Mater. Exp. 3 (2013) 61-70.

XXI. L. Eyring (Ed.), Progress in the Science and Technology of the Rare Earths, Pergamon, London (1966).

XXII. M.J. Plodinec, Borosilicate glass for nuclear waste immobilisation, Glass Technol. 41(2000), 186-192.

XXIII. M. Kemere, J. Sperga, U. Rogulis, G. Krieke, J. Grube, Structural and optical studies on Sm3+ ions doped bismuth fluoroborate glasses for visible laser applications, J. Lumin. 181 (2017) 25–30.

XXIV. M. Sundara Rao, V. Sudarsan, M.G. Brik, Y. Gandhi, K. Bhargavi, M. Piasecki, I.V. Kityk, N. Veeraiah, De-quenching influence of aluminum ions on Y/B ratio of Dy3+ ions in lead silicate glass matrix, Journal of Alloys and Compounds 575 (2013) 375-381.

XXV. M.V. Vijaya Kumar, B.C. Jamalaiah, K. Rama Gopal, R.R. Reddy., “Optical absorption and fluorescence studies of Dy3+-doped lead telluroborate glasses”, J. Lumin. 132 (2012) 86-90.

XXVI. Nisha Deopa, A.S. Rao, Photoluminescence and energy transfer studies of Dy3+ ions doped lithium lead alumino borate glasses for w-LED and laser applications, J. of Luminescence 192 (2017) 832–841.

XXVII. N. Kiran, A. Suresh Kumar., “White light emission from Dy3+ doped sodium lead borophosphate glasses under UV light excitation”, J. Mol. Struct. 1054 (2013) 6-11.

XXVIII. P. Suthanthirakumar, K. Marimuthu, Investigations on spectroscopic properties of Dy3+ doped zinc telluro-fluoroborate glasses for laser and white LED application,J. Mol. Struct. 1125 (2011) 443-452.

XXIX. R.C. Lucacel, I. Ardelean, FT-IR and Raman study of silver lead borate-based glasses, J. Non-Cryst. Solids. 353 (2007) 2020-2024.
XXX. S. Abed, H. Boughrraf, K. Bouchouit, Z. Sofiani, B. Derkowska, M.S. Aida, B. Sahraoui, Influence of Bi doping on the electrical and optical properties of ZnO thin films, Superlattice Microstruct. 85 (2015) 370-378.

XXXI. S.D. Jackson, Continuous wave 2.9µm dysprosium-doped fluoride fiber laser, Appl. Phys. Lett. 83 (2003) 1316-1318.

XXXII. S. Gai, C. Li, P. Yang, J. Lin, Recent progress in rare earth micro/nanocrystals: soft chemical synthesis, luminescent properties, and biomedical applications, Chem. Rev. 114 (2014) 2343-2389.

XXXIII. Sk. Mahamuda, K. Swapna, P. Packiyaraj, A. Srinivasa Rao, G. Vijaya Prakash, Lasing potentialities and white light generation capabilities of Dy3+ doped oxyfluoro borate glasses, J.Lumin. 153 (2014) 382−392.

XXXIV. Sudhakar Reddy: Judd–Ofelt theory: optical absorption and NIR emission spectral studies of Nd3+: CdO–Bi2O3– B2O glasses for laser applications, J Mater Sci. 47 (2012) 772–778.

XXXV. Swapna K, Mahamuda S, Rao AS, Jayasimhadri M, Moorthy LR. Visible fluorescence Characteristics of Dy3+ doped zinc alumino bismuth borate glasses for optoelectronic devices, Ceramic Int 39 (2013) 8459–65.

XXXVI. T. Srihari, C.K. Jayasankar, Fluorescence properties and white light generation from Dy3+-doped niobium phosphate glasses, Optical Materials 69 (2017) 87-95.

XXXVII. Valluri Ravi Kumar, G. Giridhar, N. Veeraiah, Influence of modifier oxide on emission features of Dy3+ ion in Pb3O4 ‒ZnO‒P2O5 glasses, Optical Materials, 60 (2016) 594-600.

XXXVIII. W. Bi, N. Louvain, N. Mercier, J. Luc, I. Rau, B. Sahraoui, A switchable NLO organic- inorganic compound based on conformationally chiral disulfide molecules and Bi(III)I5 iodobismuthate networks, Adv. Mater. 20 (2008) 1013-1017.

XXXIX. W.T. Carnall, P.R. Fields, K.Rajnak, Electronic Energy Levels in the Trivalent Lanthanide AquoIons. I. Pr3+, Nd3+, Pm3+, Sm3+, Dy3+, Ho3+, Er3+, and Tm3+, J. Chem. Phys. 49 (1968) 4424–4442.

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Abstract:

Orthogonal Frequency Division Multiplexing (OFDM) is one of the best techniquesfor improving bandwidthefficiently and combating multipath fading by choosing proper modulation scheme in wireless communications. However, this technique has a major drawback of   high Peak-to-Average Power Ratio (PAPR) which makes transmitter section inefficient by leading to power inefficiency in the Radio Frequency section Therefore OFDM with high PAPR makes the high power amplifier nonlinear and decreases efficiency of power and generates a nonlinear distorted output, and thereby reducing performance of both spectral efficiency and energy efficiency. These drawbacks of OFDM can be mostly reduced by using proposed 5G transmission schemes.

Keywords:

PAPR,5G,Spectral efficiency,OFDM,Radiofrequency,

Refference:

I. A Lomayev, A Maltsev, A Khoryaev, A Sevastyanov, R Maslennikov, in 7th IEEE Consumer Communications and Networking Conference. Comparisonof Power Amplifier Non-Linearity Impact on 60 GHz Single Carrier andOFDM Systems, (2010), pp. 1–5. doi:10.1109/CCNC.2010.5421601

II. G Fettweis, S Bittner, M Krondorf, in 69th IEEE Vehicular Technology Conference. GFDM – Generalized Frequency Division Multiplexing, (2009),pp. 1–4. doi:10.1109/VETECS.2009.5073571

III. H Bouhadda, H Shaiek, D Roviras, Y Medjahdi, R Bouallegue, Theoretical analysis of BER performance of nonlinearly amplified FBMC/OQAM and OFDM signals. EURASIP J. Adv. Signal Process. 2014(1), 1–16 (2014). doi:10.1186/1687-6180-2014-60

IV. JG Andrews, W Choi, S Buzzi, SV Hanly, A Lozano, ACK Soong, JC Zhang, What will 5G be? IEEE J. Sel. Areas Commun. 32(6), 1065–1082 (2014). doi:10.1109/JSAC.2014.2328098

V. K.Mahender, K.S. Ramesh T.Anilkumar. “Simple Transmit Diversity Techniques for Wireless Communications”, Smart Innovations in Communication and Computational Sciences, Advances in Intelligent Systemsand Computing 669, https://doi.org/10.1007/978-981-10-8968-8_28, pp. 329-342,2019

VI. K.Mahender, K.S. Ramesh T. Anilkumar, “An Efficient OFDM system with reduced PAPR for combating multipath fading”,Journal of advanced research in dynamical and control systems.9: 1939-1948.

VII. K.Mahender, K.S. Ramesh, “PAPR analysis of fifth generation multiple access waveforms for advanced wireless communication”,International journal of engineering and technology,Vol 7,No.(3.34) (2018) 487-490

VIII. K.Mahender, T.Anilkumar, “AN EFFICIENT FBMC BASED MODULATION FOR FUTUREWIRELESS COMMUNICATIONS”,ARPN Journal of engineering and applied science,ISSN 1819-6608,vol 13,no.24,DEC-2018

IX. K.S. Ramesh, K.Mahender, T.Anilkumar, “Analysis of Multipath Channel Fading Techniques in Wireless Communication systems”, American Institute of Physics,AIP Conference Proceedings1952, 020050; doi: 10.1063/1.5032012.

X. MG Bellanger, FBMC physical layer: a primer. Technical report, PHYDYAS (2010). http://www.ict-phydyas.org/teamspace/internal-folder/FBMCPrimer_ 06-2010.pdf. Accessed 4 Oct 2016

XI. M Matthe, I Gaspar, D Zhang, G Fettweis, in 2015 IEEE 82nd Vehicular Technology Conference (VTC2015-Fall). Near-ml detection for mimo-gfdm,(2015), pp. 1–2. doi:10.1109/VTCFall.2015.7391033

XII. T. Anilkumar, K.Mahender, K.S. Ramesh, “Performance study of OFDM over Multipath Fading channels for next Wireless communications”,International journal of applied engineering research , ISSN 0973-4562, 12(20): 10205-10210.

XIII. T. Anilkumar, K.Mahender, K.S. Ramesh, “SER and BER Performance analysis of digital modulation scheme over multipath fading channel”,Journal of Advanced Research in Dynamical and Control Systems,vol 9,issue 2,pp 287-291

XIV. T Wild, F Schaich, Y Chen, in 79th IEEE Vehicular Technology Conference. Waveform Contenders for 5G—Suitability for Short Packet and Low Latency Transmissions, (2014), pp. 1–5. doi:10.1109/VTCSpring.2014.7023145

XV. T Wild, D Zhang, F Schaich, Y Chen, in 19th International Conference on Digital Signal Processing. 5G Air Interface Design Based on Universal Filtered(UF-)OFDM, (2014), pp. 699–704. doi:10.1109/ICDSP.2014.6900754

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Abstract:

The Generalized Additive Model is found to be a convenient framework due of its flexibility in non-linear predictor specification.  It is possible to combine several forms of smooth plus Gaussian random effects and use numerically accurate and wide-ranging fitting smoothness estimates. The Newton interpretation of smoothing provides standardized interval approximations.  The Model assortment through additional selection penalties and p-value estimates is proposed along with bivariate combination of input variables capturing different non-linear relationship. The proposed extension includes, using non-exponential family distribution, orderly categorical models, negative binomial distributions, and multivariate additive models, log-likelihood based on Laplace and Newton models. The general problem is that there is not one particular architecture do everything with an exponential GAM family.

Keywords:

Generalized Additive Model,Newton Approximation, Laplace,Diabetic Retinopathy,

Refference:

I. Baquero OS, Santana LM, Chiaravalloti-Neto F. Dengue forecasting in São Paulo city with generalized additive models, artificial neural networks and seasonal autoregressive integrated moving average models. PloS one. 2018;13(4).

II. da Silva Marques D, Costa PG, Souza GM, Cardozo JG, Barcarolli IF, Bianchini A. Selection of biochemical and physiological parameters in the croaker Micropogoniasfurnieri as biomarkers of chemical contamination in estuaries using a generalized additive model (GAM). Science of The Total Environment. 2019 Jan 10;647:1456-67.

III. Diankha O, Thiaw M. Studying the ten years variability of Octopus vulgaris in Senegalese waters using generalized additive model (GAM). International Journal of Fisheries and Aquatic Studies. 2016;2016:61-7.
IV. Falah F, GhorbaniNejad S, Rahmati O, Daneshfar M, Zeinivand H. Applicability of generalized additive model in groundwater potential modelling and comparison its performance by bivariate statistical methods. Geocarto international. 2017 Oct 3;32(10):1069-89.

V. Gu C. Smoothing spline ANOVA models: R package gss. Journal of Statistical Software. 2014 Jun 30;58(5):1-25.

VI. Hastie T, Tibshirani R. Generalized additive models for medical research. Statistical methods in medical research. 1995 Sep;4(3):187-96.

VII. Jiang Y, Gao WW, Zhao JL, Chen Q, Liang D, Xu C, Huang LS, Ruan LM. Analysis of influencing factors on soil Zn content using generalized additive model. Scientific reports. 2018 Oct 22;8(1):1-8.

VIII. Li S, Zhai L, Zou B, Sang H, Fang X. A generalized additive model combining principal component analysis for PM2. 5 concentration estimation. ISPRS International Journal of Geo-Information. 2017 Aug;6(8):248.

IX. Matsushima S. Statistical learnability of generalized additive models based on total variation regularization. arXiv preprint arXiv:1802.03001. 2018 Feb 8.

X. Pedersen EJ, Miller DL, Simpson GL, Ross N. Hierarchical generalized additive models: an introduction with mgcv. PeerJ Preprints; 2018 Nov.

XI. Ravindra K, Rattan P, Mor S, Aggarwal AN. Generalized additive models: Building evidence of air pollution, climate change and human health. Environment international. 2019 Nov 1;132:104987.

XII. Tanskanen J, Taipale S, Anttila T. Revealing hidden curvilinear relations between work engagement and its predictors: Demonstrating the added value of generalized additive model (GAM). Journal of Happiness Studies. 2016 Feb 1;17(1):367-87.

XIII. Wood SN. Generalized additive models: an introduction with R. Chapman and Hall/CRC; 2017 May 18.

XIV. Yoon H. Effects of particulate matter (PM10) on tourism sales revenue: A generalized additive modeling approach. Tourism Management. 2019 Oct 1;74:358-69.

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Abstract:

Name Data Networking is a future Internet architecture and it depends on data. NDN takes advantage of the current Internet Architecture and aims to address the weaknesses. In NDN, interest messages are used to retrieve data. Each data has a name that is embedded inside each interest packet. Routers use these names to forward the messages as NDN does not use source or destination address. For each interest packet, a packet is issued that is called a Data packet or D-packet. D-pkt holds the name of the content and the data itself. In this paper, we propose a data structure which is the hybrid of Cuckoo filter and Trie for the name lookup process in NDN.

Keywords:

NDN model,Cuckoo Filter based Name Lookup, Bloom Filter-Based Name Lookup, NDN forwarding Overview , Routing and Forwarding in Name Data Networking,

Refference:

I. Amadeo M, Campolo C, Molinaro A. Forwarding strategies in named data wireless ad hoc networks: Design and evaluation. Journal of Network and Computer Applications. 2015 Apr 1;50:148-58.
II. Bacanin N. An object-oriented software implementation of a novel cuckoo search algorithm. InProc. of the 5th European Conference on European Computing Conference (ECC’11) 2011 Apr 28 (pp. 245-250).
III. DiBenedetto S, Papadopoulos C, Massey D. Routing policies in named data networking. InProceedings of the ACM SIGCOMM workshop on Information-centric networking 2011 Aug 19 (pp. 38-43).
IV. Ding W, Yan Z, Deng RH. A survey on future Internet security architectures. IEEE Access. 2016 Jul 29;4:4374-93.
V. Fan B, Andersen DG, Kaminsky M, Mitzenmacher MD. Cuckoo filter: Practically better than bloom. InProceedings of the 10th ACM International on Conference on emerging Networking Experiments and Technologies 2014 Dec 2 (pp. 75-88).
VI. Massawe EA, Du S, Zhu H. A scalable and privacy-preserving named data networking architecture based on Bloom filters. In2013 IEEE 33rd International Conference on Distributed Computing Systems Workshops 2013 Jul 8 (pp. 22-26). IEEE.
VII. Mun JH, Lim H. Cache sharing using bloom filters in named data networking. Journal of Network and Computer Applications. 2017 Jul 15;90:74-82.
VIII. Najafimehr M, Ahmadi M. SLCF: Single-hash lookup cuckoo filter. Journal of High Speed Networks. 2019(Preprint):1-2.
IX. Pan J, Paul S, Jain R. A survey of the research on future internet architectures. IEEE Communications Magazine. 2011 Jun 30;49(7):26-36.
X. Quan W, Xu C, Guan J, Zhang H, Grieco LA. Scalable name lookup with adaptive prefix bloom filter for named data networking. IEEE Communications Letters. 2013 Dec 6;18(1):102-5.
XI. Saxena, D., Raychoudhury, V., Suri, N., Becker, C. and Cao, J., 2016. Named data networking: a survey. Computer Science Review, 19, pp.15-55.
XII. WangL, Hoque AK, Yi C, Alyyan A, Zhang B. OSPFN: An OSPF based routing protocol for named data networking. Technical Report NDN-0003; 2012 Jul 25.
XIII. Wang L, Lehman V, Hoque AM, Zhang B, Yu Y, Zhang L. A secure link state routing protocol for NDN. IEEE Access. 2018 Jan 4;6:10470-82.
XIV. Yi C, Afanasyev A, Moiseenko I, Wang L, Zhang B, Zhang L. A case for stateful forwarding plane. Computer Communications. 2013 Apr 1;36(7):779-91.
XV. Yi C, Afanasyev A, Wang L, Zhang B, Zhang L. Adaptive forwarding in named data networking. ACM SIGCOMM computer communication review. 2012 Jun 26;42(3):62-7.
XVI. Yi C, Abraham J, Afanasyev A, Wang L, Zhang B, Zhang L. On the role of routing in named data networking. InProceedings of the 1st ACM Conference on Information-Centric Networking 2014 Sep 24 (pp. 27-36).
XVII. Yuan H, Song T, Crowley P. Scalable NDN forwarding: Concepts, issues and principles. In2012 21st International Conference on computer communications and networks (ICCCN) 2012 Jul 30 (pp. 1-9). IEEE.

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Abstract:

K-means clustering algorithm and its variants have many drawbacks and one of the major one is getting stuck at local optima while calculating centroids over random values. Algorithms that optimize computation are iterative in nature for speeding up the process of creation or search of data by multiple search agents. Swarm intelligence (SI), is a primary aspect of artificial intelligence that comprises of high complexity problems and proposed solutions that are sub-optimal and achievable in a given time span. SI adopts cooperative character of an organized group of animals that are formed on the phrase: strive to survive and in this paper we provide a detailed survey of eight different SI algorithms that are related to insect and animal based algorithms and provides initial understanding and exploring of technical aspects of algorithms.

Keywords:

Swarm intelligence,Machine learning, K-means,Bio-inspired algorithms,Intelligent algorithms, Literature review,Nature-inspired computing,

Refference:

I. B. Ozden, S. Ramaswamy, A. Silberschatz. Cyclic associ-ation rules. Proceedings of the 15 th International Conference on Data Engineering. 1998, 412-421.
II. D. R. Li, S. L. Wang, W. Z. Shi et al. On Spatial Data Mining and Knowledge Discovery [J]. Geomatics and Information Science of Wuhan University, 2001, 26(6): 491-499.
III. Dr. Seena Naik, “An Effective use of Data Mining Techniques to Creation”, International Journal of Advancement in Engineering, OCT, 2016, volume:3, Edition:10, pp.157-163, ISSN:2349-3224.
IV. D. Ramesh, Syed Nawaz Pasha, G.Roopa,”A Comparative Analysis of Classification Algorithms on Weather Dataset Using Data Mining Tool”, Oriental Journal of Computer Science and Technology, DEC, 2017, Volume:10, Issue:4, Pp.1-5, ISSN:0974-6471.
V. Forsyth P, Wren A (1997) an ant system for bus driver scheduling. Research Report 97.25, University of Leeds School of Computer Studies
VI. J. Han, G. Dong, Y. Yin. Efficient mining of partial periodic patterns in time series database. In Proc. 1999 Int. Conf. Data Engineering (ICDE’99), pages 106-115, Sydney, Australia, April 1999.
VII. Ji, Xue, et al. “PRACTISE: Robust prediction of data center time series.” International Conference on Network & Service Management 2015. G. Box, G. M. Jenkins. Time series analysis: Forecasting and control, Holden Day Inc., 1976.
VIII. Kuo RJ, Chiu CY, Lin YJ (2004) Integration of fuzzy theory and ant algorithm for vehicle routing problem with time window. In: IEEE annual meeting of the fuzzy information, 2004. Processing NAFIPS’04, vol 2, pp 925–930. IEEE
IX. Mishra A, Agarwal C, Sharma A, Bedi P (2014) Optimized gray-scale image watermarking using DWT-SVD and firefly algorithm. expert syst appl 41(17):7858–7867
X. Mohammed Ali Shaik, “A Survey on Text Classification methods through Machine Learning Methods”, International Journal of Control and Automation, Vol. 12, No.6, (2019), pp. 390 – 396.
XI. Mohammed Ali Shiak, “A Survey of Multi-Agent Management Systems for Time Series Data Prediction”, International Journal of Grid and Distributed Computing, Vol. 12, No. 3, (2019), pp. 166-171.
XII. Mohammed Ali Shaik, “Time Series Forecasting using Vector quantization”, International Journal of Advanced Science and Technology, Vol. 29, No. 4, (2020), pp. 169-175.
XIII. Mohammed Ali Shaik, S Narsimha Rao, Abdul Rahim, “A SURVEY OF TIME SERIES DATA PREDICTION ON SHOPPING MALL”, Indian Journal of Computer Science and Engineering (IJCSE),Vol. 4 No.2 Apr-May 2013, ISSN : 0976-5166, pp. 174-184.
XIV. 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.
XV. Nakamura RY, Pereira LA, Costa KA, Rodrigues D, Papa JP, Yang XS (2012) BBA: a binary bat algorithm for feature selection. In 2012 25th SIBGRAPI conference on graphics, patterns and images. IEEE, pp 291–297
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Abstract:

This study aims to test a new treatment that has been developed for type 2 diabetes, by estimating the response of diabetics by experimenting number of mixed linear models, non-parametric, where they were compared by relying on the coefficient of determination and the standard error for the random errors in order to determine the appropriate model and then measure the effectiveness This new treatment is for type 2 diabetes. Therefore, some non-parametric methods were used in estimating the average response for the mixed linear model. The method of the kernel smoothing function was used by employing the Gaussian and Epanchnikov family functions, as well as some formulas of the Cross Validation method. To estimate Bandwidth as Scott and Silverman. An experiment for a new treatment for type 2 diabetes was chosen as an application of the mixed linear model, by experimenting with this drug on a sample of patients who were divided into three different age groups and performing laboratory tests for a period of three months, and then estimating their response rates to the new drug through four models Different. The results demonstrated that the A mixed non-parametric linear model with (Gaussian) function and the (Scott) package was the best fit model for this study, as it gave the largest determination coefficient and the lowest standard deviation of the error, as well as the new drug, was not effective in regulating blood sugar level for all age groups of patients.

Keywords:

Linear mixed model Non-parametric ,Kernel Smoothing,Bandwidth,

Refference:

I. Carroll, R.J., Delaigle, A. and Hall, P. (2007). “Nonparametric Regression Estimation from Data Contaminated by a Mixture of Berkson and Classical Errors”. Journal of the Royal Statistical Society, 69, 859-878.
II. Czado, C, (2007). “Linear Models with Random Effects”. Lecture notes, web paper.
III Hardle, W., (1994), “Applied Nonparametric Regression”. Humboldt – University, Berlin, Germany.
IV Heather, T., (2008). “Introduction to Generalized Linear Models”. ESRC National Centre for Research Methods, University of Warwick, UK.
V Jiang, J. (2007).” Linear and Generalized Linear Mixed Models and Their Applications”. Springer, New York.
VI McCullagh, P., and Nelder, J. A‖ Generalized Linear Models‖, (1989). 2nd ed. London: Chapman& Hall.
VII Nelder, J., Wedderburn.W., (1972).” Generalized Linear Models‖, Journal of the Royal Statistical Society. Series A (General), 135(3), 370-384.
VIII Racine J, Li, Q., (2004). “Nonparametric Estimation of Regression Functions with both Categorical and Continuous Data.” Journal of Econometrics, 119(1), 99–130.
IX Racine JS, Li Q, Zhu X (2004). “Kernel Estimation of Multivariate Conditional Distributions.”. Annals of Economics and Finance, 5(2), 211–235.
X – Wand M, Ripley, B., (2008).” Kern Smooth Functions for Kernel Smoothing R package”, version 2.22-22, URL http://CRAN.R-project.org/package=KernSmooth.
XI – Watson, G., (1964). “Smooth Regression Analysis.” Sankhya, 26(15), 359–372.
XII- Yin, Z., Liu, F.& Xie, Y., (2016). “Nonparametric Regression Estimation with Mixed Measurement Errors “. Applied Mathematics, (7), 2269-2284.

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Abstract:

In a very competitive and fast emerging IT and wireless technology need a company to move fast and it demands the company to have the correct decision support system in choosing suppliers. The right system helps company to gain useful and meaningful data in making the right decision of selecting the right suppliers which helps them to improve their performance and sustainable in the industry that they are involve in. In making the right decision of selecting suppliers the factors of efficiency and effectiveness of the decision support system used have to be concerned.In this paper, different selection methods considering their effectiveness and efficiency systems used in choosing suppliers is discussed.

Keywords:

Support System (DSS), System Enterprise, Information Systems,Supplier selection,Business Sustainable,

Refference:

I Agnieszka Konys 2019, Methods Supporting Supplier Selection Processes – Knowledge-based Approach,Procedia Computer Science, Volume 159, Pages 1629-1641

II El-Ebiary, Y. A. B., Al-Sammarraie, N. A. & Saany, S. I. A. (2019). “Analysis of Management Information Systems Reports for Decision-Making”. (IJRTE), 8(IC2), pp. 1150-1153.

III El-Ebiary, Y. A. B., Al-Sammarraie, N. A., & Saany, S. I. A. (2019). “The Implementation of M-Commerce in Supply Chain Management System”. 3C Tecnologia, May, pp. 223-240.

IV El-Ebiary, Y.; Najam, I.; Abu-Ulbeh, W. (2018). The Influence of Management Information System (MIS) in Malaysian’s Organisational Processes—Education Sector, Advanced Science Letters, 24(6), pp. 4129-4131(3).

V Galankashi, M. R., Helmi, S. A., & Hashemzahi, P. (2016). Supplier selection in automobile industry: A mixed balanced scorecard–fuzzy AHP approach. Alexandria Engineering Journal, 55(1), 93-100.

VI Kar, A. K. (2015). A hybrid group decision support system for supplier selection using analytic hierarchy process, fuzzy set theory and neural network. Journal of Computational Science, 6, 23–33. doi:10.1016/j.jocs.2014.11.002

VII Konys, A. (2018). An Ontology-Based Knowledge Modelling for a Sustainability Assessment Domain. Sustainability, 10(2), 300. doi:10.3390/su10020300

VIII Li, J., Sun, M., Han, D., Wu, X., Yang, B., Mao, X., & Zhou, Q. (2018). Semantic multi-agent system to assist business integration: An application on supplier selection for shipbuilding yards. Computers in Industry, 96, 10–26. doi:10.1016/j.compind.2018.01.001

IX Mirchandani, D., & Pakath, R. (1999). Four models for a decision support system. Information & Management, 35(1), 31–42. doi:10.1016/s0378-7206(98)00074-3

X Polat, G., & Eray, E. (2015). An integrated approach using AHP-ER to supplier selection in railway projects. Procedia Engineering, 123, 415-422.

XI Scott, J., Ho, W., Dey, P. K., & Talluri, S. (2015). A decision support system for supplier selection and order allocation in stochastic, multi-stakeholder and multi-criteria environments. International Journal of Production Economics, 166, 226-237.

XII Shi, P., Yan, B., Shi, S., & Ke, C. (2015). A decision support system to select suppliers for a sustainable supply chain based on a systematic DEA approach. Information Technology and Management, 16(1), 39-49.

XIII Sivakumar, R., Kannan, D., & Murugesan, P. (2015). Green vendor evaluation and selection using AHP and Taguchi loss functions in production outsourcing in mining industry. Resources Policy, 46, 64-75.

XIV YAB EL-EBIARY (2016). Management Information Systems and Their Importance in the Decision-Making. International Journal of Latest Engineering and Management Research (IJLEMR), 1(7) PP. 10-14.

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Abstract:

Management Information System (MIS) is the use of information technology, people, and business processes to record, store and process data to produce data-driven information that helps managers to derive decisions for the organizations.The decision is consciously taken from a variety of alternatives and the consent of many is based on the goal of achieving the desired outcome. MIS can be defined as a collection of systems, hardware, procedures, and people that all work together to process, store, and produce information that is useful to the organization. It is an important system for every organization that needsto have to ensure they remain competitive in the market. However, not all MIS fulfil the requirements from stakeholders. Some have failed to do so due to several factors such as poor requirement design or improper training to the users. Therefore, in this study, the paper focus to identify the key criteria that contribute to effectiveness in developing the “fit” MIS based on previous studies. The criteria discussed in detail by hoping this find out will become major guidelines to create a good MIS.

Keywords:

Management Information System (MIS),Information Systems,Middle Management,Enterprise Systems, Decision-Making,

Refference:

I Al Shobaki, M. J., & Abu-Naser, S. S. (2017). The Requirements of Computerized Management Information Systems and Their Role in Improving the Quality of Administrative Decisions in the Palestinian Ministry of Education and Higher Education.

II Amuna, Y. M. A., Al Shobaki, M. J., & Naser, S. S. A. (2017). The Role of Knowledge-Based Computerized Management Information Systems in the Administrative Decision-Making Process.

III Babaei, M., & Beikzad, J. (2013). Management information system, challenges, and solutions. European Online Journal of Natural and Social Sciences: Proceedings, 2(3 (s)), pp-374.

IV Bendre, M. P., Murukate, M. P., Desai, M. V., Dhenge, M. D., & Kelkar, M. B. (2017). Management Information System.

V Berisha-Shaqiri, A. (2015). Management Information System and Competitive Advantage. Mediterranean Journal of Social Sciences, 6(1), 204.

VI Chițescu, R. I. (2015). Informational Management System and Its Challenges at Decision Level. SEA–Practical Application of Science, 3(08), 33-38.

VII Djilali, B. (2017). The Effect of Information Systems Efficiency On Effectiveness of Decision Making: A Field Study in Algerian Banks.

VIII El-Ebiary, Y. A. B., Al-Sammarraie, N. A. & Saany, S. I. A. (2019). “Analysis of Management Information Systems Reports for Decision-Making”. (IJRTE), 8(IC2), pp. 1150-1153.

IX El-Ebiary, Y.; Najam, I.; Abu-Ulbeh, W. (2018). The Influence of Management Information System (MIS) in Malaysian’s Organisational Processes—Education Sector, Advanced Science Letters, 24(6), pp. 4129-4131(3).

X Elhadi, O. A., & Quanxiu, L. (2013). Public sector employees’ view (s) of obstacles facing the development of Management Information Systems in the River Nile State-Sudan.

XI Furduescu, B. A. (2017). Management Information Systems. HOLISTICA–Journal of Business and Public Administration, 8(3), 61-70.

XII Hakimpoor, H., &Khairabadi, M. (2018).Management Information Systems, Conceptual Dimensions of Information Quality and Quality of Managerial Decisions: Modelling Artificial Neural Networks.

XIII Ijoema, M. M. (2018). Importance of Management Information System in service Delivery and Paper Work in Nigeria University. IOSR Journal of Business and Management, 20(9), 30-38.

XIV Kalhoro, S., Rahoo, L. A., Kalhoro, M., & Nagar, M. A. K. (2019). The Meaning and Role of Management Information System in the Telecom Companies in Sindh Province.

XV Kim, G. H., Trimi, S., & Chung, J. H. (2014). Big-data applications in the government sector. Communications of the ACM, 57(3), 78-85.

XVI Mahasneh, M. S. (2015). The Importance of Management Information Systems in Decision-Making Process in Najran University.

XVII Mohammed, A. N. N. A. M., & Hu, W. (2015). Using Management Information Systems (MIS) to Boost Corporate Performance. International Journal of Management Science and Business Administration, 1(11), 55-61.

XVIII Sarveswaran, K., Perera, P., Nanayakkara, S., Perera, A., & Fernando, S. (2006). Challenges in developing MIS–Case from Government sector.

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