Archive

Abstract:

Usually, the order of kinetics of thermoluminescence (TL) glow curve is evaluated by using the concept of traditional symmetry factor (μ_g) in which only three points of a glow curve are used. From the statistical point of view of the reliability of any method of analysis of glow, curve improves if instead of a few points the method can use a larger portion of the glow curve. In the present work, a technique is proposed to determine the order of kinetics associated with a TL peak by using the concept of skewness. The method is applied to experimental thermoluminescence (TL) curves recorded in a hyperbolic heating scheme.

Keywords:

Thermoluminescence,hyperbolic heating scheme,skewness,order of kinetics,

Refference:

I. C. Christodoulides, J Phys D 19, 1555 (1986).
II. C. Christodoulides, Phys Stat Sol (a) 94, 251 (1986).
III. C. M. Sunta, Unravelling Thermoluminescence, (Springer), New Delhi (2015).
IV. G. F. G. Garlick and A. F. Gibson, Proc. Phys. Soc. 40, 574 (1948).
V. J. T. Randall and M. H. F. Wilkins, Proc. Roy. Soc. A 184, 366 (1945).
VI. M. Abramowitz and I. A. Stegun, Handbook of Mathematical functions, Dover, New York (1972).
VII. M. R. Spigel and L. J. Stephens, Theory and Problems of Statistics, 3rd edition, (Tata McGrawHill), New Delhi (2000).
VIII. N. G. Das, Statistical Methods, (McGrawHill Education), New Delhi, (2012).
IX. R. Chen and S. W. S. McKeever, Theory of Thermoluminescence and Related Phenomena, (World Scientific, Singapore), (1997).
X. R. Chen, J. Electrochem. Soc. 116, 1254 (1969).
XI. R. Chen and Y. Kirsh, : ‘Analysis of Thermally stimulated Processes’, (Pergamon, Oxford), (1981).
XII. D. C. Sanyal and K. Das, : ‘Introduction to Numerical Analysis’, (U. N. Dhar, Kolkata), (2012).
XIII. S. K. Azharuddin, B. Ghosh S. Ghosh and P. S. Majumdar. : ‘On the Suitability of Peak Shape Method for the Analysis of Thermoluminescence in Different Models’, J. Mech. Cont. & Math. Sci., Vol.-13, No.-4, September-October (2018) Pages 29-38.
XIV. S. K. Azharuddin, B. Ghosh, A. Sarkar, S. Bhattacharyya and P. S. Majumdar. : ‘On the applicability of Initial Rise and Peak Shape methods for Thermoluminescence peaks recorded under hyperbolic heating profile for OTOR and IMTS models’. J. Mech. Cont. & Math. Sci., Vol.-14, No.2, March-April (2019) pp 121-131. DOI : 10.26782/jmcms.2019.04.00010
XV. S. K. Azharuddin, S. D. Singh and P. S. Majumdar. : ‘ON THE PEAK SHAPE METHOD OF THE DETERMINATION OF ACTIVATION ENERGY AND ORDER OF KINETICS IN THERMOLUMINESCENCE RECORDED WITH HYPERBOLIC HEATING PROFILE’. J. Mech. Cont. & Math. Sci., Vol.-12, No.-2, January (2018) Pages 10-20.
XVI. W. M. Ziniker, J. M. Rustin and T. G. Stoebe, J. Material Sci. 8, 407 (1973).

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Abstract:

Fuzzy entropy is being used to measure the uncertainty with high precision and accuracy than classical crisp set theory. It plays a vital role in handling complex daily life problems involving uncertainty. In this manuscript, we first review several existing entropy measures and then propose novel entropy to measure the uncertainty of a fuzzy set. We also construct an axiomatic definition based on the proposed entropy measure. Numerical comparison analysis is carried out with existing entropies to show the reliability and practical applicability of our proposed entropy measure. Numerical results show that our suggested entropy is reasonable and appropriate in dealing with vague and uncertain information. Finally, we utilize our proposed entropy measure to construct fuzzy TOPSIS (Technique for Ordering Preference by Similarity to Ideal Solution) method to manage Multicriteria decision-making problems related to daily life settings. The final results demonstrate the practical effectiveness and applicability of our proposed entropy measure

Keywords:

Fuzzy sets,Entropy measure,Uncertainty,TOPSIS,Multicriteria decision making,

Refference:

I. Chen, S. M., Cheng, S. H., & Chiou, C. H. (2016). Fuzzy multiattribute group decision making based on intuitionistic fuzzy sets and evidential reasoning methodology. Information Fusion, 27, 215-227.
II. De Luca, A., & Termini, S. (1972). A definition of a non-probabilistic entropy in the setting of fuzzy sets theory. Information and control, 20(4), 301-312.
III. Ebanks, B. R. (1983). On measures of fuzziness and their representations. Journal of Mathematical Analysis and Applications, 94(1), 24-37.
IV. Fan, J. L., & Ma, Y. L. (2002). Some new fuzzy entropy formulas. Fuzzy sets and Systems, 128(2), 277-284.
V. Higashi, M., & Klir, G. J. (1982). On measures of fuzziness and fuzzy complements.
VI. Hussain, Z., & Yang, M. S. (2018). Entropy for hesitant fuzzy sets based on Hausdorff metric with construction of hesitant fuzzy TOPSIS. International Journal of Fuzzy Systems, 20(8), 2517-2533.
VII. Hwang, C. L., & Yoon, K. (1981). Methods for multiple attribute decision making. In Multiple attribute decision making, 58-191.
VIII. Hwang, W.L. & Yang, M-S. (2008). On entropy of fuzzy sets, Int. J. of Uncertainty, Fuzziness and Knowledge-Based Sys, 4, 519 –527.
IX. Joshi, R., & Kumar, S. (2016). A new approach in multiple attribute decision making using R-norm entropy and Hamming distance measure. International Journal of Information and Management Sciences, 27(3), 253-268.
X. Kosko, B. (1986). Fuzzy entropy and conditioning. Information sciences, 40(2), 165-174.
XI. Li, P., & Liu, B. (2008). Entropy of credibility distributions for fuzzy variables. IEEE Transactions on Fuzzy Systems, 16(1), 123-129.
XII. Liang, D., & Xu, Z. (2017). The new extension of TOPSIS method for multiple criteria decision making with hesitant Pythagorean fuzzy sets. Applied Soft Computing, 60, 167-179.

XIII. Meng, F., & Chen, X. (2014). An approach to interval-valued hesitant fuzzy multi-attribute decision making with incomplete weight information based on hybrid Shapley operators. Informatica, 25(4), 617-642.
XIV. Pal, N. R., & Pal, S. K. (1992). Higher order fuzzy entropy and hybrid entropy of a set. Information Sciences, 61(3), 211-231.
XV. Razia Sharif, Zahid Hussain, Shahid Hussain, Sahar Abbas, Iftikhar Hussain, ‘A NOVEL FUZZY ENTROPY MEASURE AND ITS APPLICATION IN COVID-19 WITH FUZZY TOPSIS’. J. Mech. Cont. & Math. Sci., Vol.-16, No.-6, June (2021) pp 52-63. DOI : 10.26782/jmcms.2021.06.00005.
XVI. Shannon, C. E. (1948). A mathematical theory of communication. The Bell system technical journal, 27(3), 379-423.
XVII. Xu, L., & Yang, J. B. (2001). Introduction to multi-criteria decision making and the evidential reasoning approach (Vol. 106). Manchester: Manchester School of Management.
XVIII. Yager, R. R. (1979). On the measure of fuzziness and negation part I: membership in the unit interval, 221-229.
XIX. Zadeh, L. A. (1965). Information and control. Fuzzy sets, 8(3), 338-353.
XX. Zadeh, L.A. (1975). The concept of a linguistic variable and its application to approximate reasoning—II. Information sciences, 8(4), 301–357.
XXI. Zahid Hussain, Sahar Abbas, Shahid Hussain, Zaigham Ali, Gul Jabeen. : ‘SIMILARITY MEASURES OF PYTHAGOREAN FUZZY SETS WITH APPLICATIONS TO PATTERN RECOGNITION AND MULTICRITERIA DECISION MAKING WITH PYTHAGOREAN TOPSIS’. J. Mech. Cont. & Math. Sci., Vol.-16, No.-6, June (2021) pp 64-86. DOI : 10.26782/jmcms.2021.06.00006.
XXII. Zimmermann, H.J. (1991). Fuzzy set theory and its applications, Google Scholar Google Scholar Digital Library Digital Library, (1985).

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Abstract:

Twitter has now become the most common social platform to express views on any topic. A micro-blogging social media offers a way for people around the world to show their sentiments about any political, social and cultural subject of the time. In this paper, the sentimental analysis approach has been used to analyze the positive and negative sentiments of Twitter users about some top trending #tags around the globe. The data has been collected between the duration of March to April 2021. The collected data were processed by using the Python program and then transformed our data set with the help of the SQL database. We have used graphs and tables to present the data, collected under three hashtags; which were top trending topics on that particular era. The tweets were elaborated by positive, negative and neutral sentiments which were depicted in graphs. It is clear from the results and comparison that social media has a strong influence in the present era and can be highly helpful to use as a predictor of any political, social situation prevailing in any country or worldwide. It has also been helpful for business communities to analyze their products in the same manner to improve their business growth.

Keywords:

social platform,social media,#tags,SQL,SA,API,

Refference:

I. A. Giachanou and F. Crestani, ‘Like It or Not: A Survey of Twitter Sentiment Analysis Methods,’ ACM Comput. Surv., vol. 49, no. 2, pp. 1-41, 2016
II. Diakopoulos, N. & Shamma, D., 2010. Characterizing Debate Performance via Aggregated Twitter Sentiment. Proceedings of the 28th int. conference on Human factors in computing systems. Go, A., Bhayani, R. & Huang, L., 2009. Twitter Sentiment Classification using Distant Supervision.”
III. https://en.m.wikipedia.org/wiki/Instagram
IV. Koyel Chakraborty, Sudeshna Sani, Rajib Bag,: ‘A STUDY ON SENTIMENT POLARITY IDENTIFICATION OF INDIAN MULTILINGUAL TWEETS THROUGH DIFFERENT NEURAL NETWORK MODELS’. J. Mech. Cont.& Math. Sci., Vol.-15, No.-1, January (2020) pp 108-117. DOI : 10.26782/jmcms.2020.01.00008.
V. Larsen, Peder Olesen, and Markus von Ins. ‘The Rate of Growth in Scientific Publication and the Decline in Coverage Provided by Science Citation Index.’, Scientometrics 84.3 (2010): 575–603. PMC. Web. 25 Sept. 2015.”
VI. Liu, Bing, and Lei Zhang. ‘A survey of opinion mining and Sentiment Analysis (SA). Mining text data. Springer, Boston, MA, 2012. 415-463.”
VII. Omar bin Md Din, Abdul Ghani Bin Md Din, Rusdee Taher, Abduloh Usof, Prasert Panprae, Yousef A. Baker El-Ebiary. : ‘WEB CONTEXT AND THE MULTIPLE SEMANTIC LINGUISTIC ORIGINS AND ITS IMPACTS ON THE PROPHET’S TEXT. J. Mech. Cont.& Math. Sci., Vol.-15, No.-7, July (2020) pp 392-404. DOI : 10.26782/jmcms.2020.07.00033.
VIII. Polk, Alexander, and Patrick Paroubek. ‘Twitter as a corpus for Sentiment Analysis (SA) and opinion mining.’ LREc. Vol. 10. No. 2010. 2010.”
IX. Selmer, Øyvind, et al. ‘NTNU: Domain semi-independent short message sentiment classification.’ Second Joint Conference on Lexical and Computational Semantics (* SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013). Vol. 2. 2013.”
X. Turney, Peter D. ‘Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews.’ Proceedings of the 40th annual meeting in association for computational linguistics. Association for Computational Linguistics, 2002.”
XI. Twitter – Wikipedia.

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Abstract:

Voltage multipliers are used to convert the low AC voltage output of energy harvesters into relatively high DC voltage for portable devices and wireless sensor nodes (WSNs) applications. DC voltage conversion is required to operate an electronic device or recharge battery. In order, to convert the low AC voltage output of the energy harvester into relatively high DC voltage, a voltage multiplier circuit need to be integrated with the energy harvester. In this study, a Prototype-1 (two-stages) and Prototype-2 (three-stage) Dickson voltage multipliers and Prototype-3 (seven-stage) Cockcroft-Walton voltage multiplier circuits are developed. The device is capable of converting a low voltage of 50 mV into 350 mV. The research focuses on the development and characterization of Prototype-1, Prototype-2 and Prototype-3 circuits. Results indicate that the determination of load resistance is important for better output power. The maximum power of 11.97 μW was obtained by prototype-3 elucidating better power compared to prototype-1 and prototype-2 and the power was obtained at an optimum load of 560 kΩ. Furthermore, a rectenna tested at different distances from the source, revealed that a prototype-2 produced a maximum power of 3.01 × 10 -6 μW, at an optimum load of 560 kΩ.

Keywords:

Voltage multipliers,energy harvesters,AC to DC,rectifier,low voltage,flow-based,RF,

Refference:

I. Ahmad M M, Khan F U. Review of vibration-based electromagnetic–piezoelectric hybrid energy harvesters. Int J Energy Res. 2020;1–40. https://doi.org/10.1002/er.6253
II. Ahmad MM, Khan NM, Khan FU. Review of frequency up-conversion vibration energy harvesters using impact and plucking mechanism. Int J Energy Res. 2021;1–37. https://doi.org/10.1002/er.6832
III. Akyildiz, I.F., et al., A survey on sensor networks. IEEE Communications magazine, 2002. 40(8): p. 102-114.
IV A. Leon-Masich, V. Hugo, M. B. Josep, M. Luis, and F. Freddy. High voltage led supply using a hysteretic controlled single stage boost converter. Przeglad Elektrotechniczny, vol. 88, no. 1, pp. 26–30, 2012.
V. Bibhu Prasad Ganthia, Subrat Kumar Barik, Byamakesh Nayak, : ‘APPLICATION OF HYBRID FACTS DEVICES IN DFIG BASED WIND ENERGY SYSTEM FOR LVRT CAPABILITY ENHANCEMENTS’. J. Mech. Cont.& Math. Sci., Vol.-15, No.-6, June (2020) pp 245-256. DOI : 10.26782/jmcms.2020.06.00019
VI. Chang, Y.H. and Y.C. Chen, Multistage multiphase switched‐capacitor DC–DC converter with variable‐phase and PWM control. International Journal of Circuit Theory and Applications, 2012. 40(8): p. 835-857.
VII. Khan F U. A vibration based electromagnetic and piezoelectric hybrid energy harvester. Int J Energy Res. 2020;44: 6894–6916. https://doi.org/10.1002/er.5442
VIII. Khan F, Sassani F, Stoeber B. Nonlinear behaviour of membrane type electromagnetic energy harvester under harmonic and random vibrations. Microsyst. Technol. 2014;20:1323-1335. http://doi.org/10.1007/s00542-013-1938-1.
IX. Khan F U, Qadir M U. State-of-the-art in vibration-based electrostatic energy harvesting. J Micromech Microeng. 2016;26:103001.
X. Khan FU, Khattak MU. Contributed Review: Recent developments in acoustic energy harvesting for autonomous wireless sensor nodes applications. Rev. Sci. Instrum. 2016;87:021501. http://doi.org/10.1063/1.4942102.
XI. Ehab Belal, Hassan Mostafa, Yehea Ismail and M. Sameh Said. A Voltage Multiplying AC/DC Converter for Energy Harvesting Applications.

XII. E. M. Ali, N. Z. Yahaya, N. Perumal, M. A. Zakariya. Development of Cockcroft‐Walton voltage multiplier for RF energy harvesting applications. Journal of Scientific Research and Development 3 (3): 47-51, 2016.
XIII. E. M. Ali, N. Z. Yahaya, N. Perumal and M. A. Zakariya. DESIGN AND DEVELOPMENT OF HARVESTER RECTENNA AT GSM BAND FOR BATTERY CHARGING APPLICATIONS. Journal of Engineering and Applied Sciences VOL. 10, NO 21, NOVEMBER, 2015.
XIV. Filiz SARI, Yunus UZUN. A COMPARATIVE STUDY: VOLTAGE MULTIPLIERS FOR RF ENERGY HARVESTING SYSTEM. Commun.Fac.Sci.Univ.Ank.Series A2-A3 Volume 61, Number 1, Pages 12-23 2019.
XV. Farid Khan, Farrokh Sassani and Boris Stoeber Copper foil-type vibration-based electromagnetic energy harvester 2010 J. Micromech. Microeng. 20 125006. https://doi.org/10.1088/0960-1317/20/12/125006
XVI. Hagerty, J.A., et al., Recycling ambient microwave energy with broad-band rectenna arrays. IEEE Transactions on Microwave Theory and Techniques, 2004. 52(3): p. 1014-1024.
XVII. Hamayun Khan, Sheeraz Ahmed, Rehan Ali Khan, S. Arhan Haider Shah, Zeeshan Najam, Hasnain Abbas, Asif Nawaz, Zubair Aslam Khan, : AN IOT BASED ENERGY OPTIMIZATION TECHNIQUE FOR ELECTRICAL EQUIPMENT’S USING WIRELESS SENSOR NETWORKS. J. Mech. Cont. & Math. Sci., Vol.-15, No.-8, August (2020) pp 628-646. DOI : 10.26782/jmcms.2020.08.00053
XVIII. Jia, J. and K.N. Leung, Improved active‐diode circuit used in voltage doubler. International Journal of Circuit Theory and Applications, 2012. 40(2): p. 165-173.
XIX. J. Zakis, D. Vinnikov, I. Roasto, and R. Strzelecki. Design guidelines of new step-up DC/DC converter for fuel cell powered distributed generation systems,” in Proceedings of the 8th International Symposium on Topical Problems in the Field of Electrical and Power Engineering, pp. 33–41, Parnu, Estonia, January 2010.
XX. Kavuri Kasi Annapurna Devi, Norashidah Md. Din, Chandan Kumar Chakrabarty. Optimization of the Voltage Doubler Stages in an RF-DC Convertor Module for Energy Harvesting. Circuits and Systems, 2012, 3, 216-222.
XXI. Khan, F.U.; Ahmad, S. Flow type electromagnetic based energy harvester for pipeline health monitoring system. Energy Convers. Manag. 2019, 200, 112089. https://doi.org/10.1016/j.enconman.2019.112089
XXII. Khan FU. Review of non-resonant vibration based energy harvesters for wireless sensor nodes. J Renewable Sustainable Energy. 2016;8:044702. http://doi.org/10.1063/1.4961370.
XXIII. Khajuria, R. and S. Gupta. Energy optimization and lifetime enhancement techniques in wireless sensor networks: A survey. in International Conference on Computing, Communication & Automation. 2015. IEEE.
XXIV. Muhammad Iqbal, Farid Ullah Khan. Hybrid vibration and wind energy harvesting using combined piezoelectric and electromagnetic conversion for bridge health monitoring applications. Energy Conversion and Management 2018 Vol. 172, Pages 611-618. https://doi.org/10.1016/j.enconman.2018.07.044
XXV. Makowski, M.S. and D. Maksimovic. Performance limits of switched-capacitor DC-DC converters. in Proceedings of PESC’95-Power Electronics Specialist Conference. 1995. IEEE.
XXVI. Mitcheson, P.D., et al., Architectures for vibration-driven micropower generators. Journal of microelectromechanical systems, 2004. 13(3): p. 429-440.
XXVII. N. M. Din, C. K. Chakrabarty, A. Bin Ismail, K. K. A. Devi, and W.-Y. Chen. DESIGN OF RF ENERGY HARVESTING SYSTEM FOR ENERGIZING LOW POWER DEVICES. Progress In Electromagnetics Research, Vol. 132, 49–69, 2012 .
XXVIII. Pillai, M.A. and E. Deenadayalan, A review of acoustic energy harvesting. International journal of precision engineering and manufacturing, 2014. 15(5): p. 949-965.
XXIX. Sakai, Y., et al., Highly efficient pulsed power supply system with a two-stage LC generator and a step-up transformer for fast capillary discharge soft x-ray laser at shorter wavelength. Review of Scientific Instruments, 2010. 81(1): p. 013303.
XXX. Samuel S. B. Hong, Rosdiazli Ibrahim, Mohd H. Md. Khir, Mohammad A. Zakariya, and Hanita Daud. WI-FI ENERGY HARVESTER FOR LOW POWER RFID APPLICATION. Progress In Electromagnetics Research C, Vol. 40, 69–81, 2013.
XXXI. Shaikh, F.K., S. Zeadally, and E. Exposito, Enabling technologies for green internet of things. IEEE Systems Journal, 2015. 11(2): p. 983-994.
XXXII. Shenck, N.S. and J.A. Paradiso, Energy scavenging with shoe-mounted piezoelectrics. IEEE micro, 2001. 21(3): p. 30-42.
XXXIII. Po-Hung Chen, Koichi Ishida, Xin Zhang, Yasuaki Okuma, Yoshikatsu Ryu, Makoto Takamiya, and Takayasu Sakurai. 0.18-V Input Charge Pump with Forward Body Biasing in Startup Circuit using 65nm CMOS. Proc. IEEE Custom Integrated Circuits Conf. (CICC), 2010.
XXXIV. Tuna, G., et al., Energy harvesting techniques for industrial wireless sensor networks. inIndustrial Wireless Sensor Networks: Applications, Protocols, Standards, and Products, GP Hancke and VC Gungor, Eds, 2017: p. 119-136.
XXXV. T. Meng, H. Ben, D. Wang, and H. Huang. Starting strategies of three-phase single-stage PFC converter based on isolated fullbridge boost topology. Przeglad Elektrotechniczny, vol. 87, no. 3, pp. 281–285, 2011.
XXXVi. Vandana Niranjan, Maneesha Gupta. Low voltage four-quadrant analog multiplier using dynamic threshold MOS transistors. Microelectronics International Volume 26 • Number 1 • 2009 • 47–52.
XXXVII. W. U. Rahman and F. U. Khan, “Modeling and Simulation of Flow-Based Circular Plate Type Piezoelectric Energy Harvester for Pipeline’s Monitoring,” 2019 22nd International Multitopic Conference (INMIC), 2019, pp. 1-6, doi: 10.1109/INMIC48123.2019.9022755.
XXXVIII. W. Li, X. Xiang, C. Li, and X. He. Interleaved high step-up ZVT converter with built-in transformer voltage doubler cell for distributed PV generation system. IEEE Transactions on Power Electronics, vol. 28, no. 1, pp. 300–313, 2013.
XXXIX. Weiser, M., The Computer for the 21 st Century. Scientific American, 1991. 265(3): p. 94-105.
XL. Y.C. Wong, P.C. Tan, M. M. Ibrahim, A.R. Syafeeza, N. A. Hamid. Dickson Charge Pump Rectifier using Ultra-Low Power (ULP) Diode for BAN Applications. Journal of Telecommunication, Electronic and Computer Engineering Vol. 8 No. 9 September – December 2016.

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Abstract:

In recent years, some improvements have been suggested in the literature that has been a better performance or nearly equal to existing numerical iterative techniques (NIT). The efforts of this study are to constitute a Numerical Hybrid Iterative Technique (NHIT) for estimating the real root of nonlinear equations in one variable (NLEOV) that accelerates convergence. The goal of the development of the NHIT for the solution of an NLEOV assumed various efforts to combine the different methods. The proposed NHIT is developed by combining the Taylor Series method (TSM) and Newton Raphson's iterative method (NRIM). MATLAB and Excel software has been used for the computational purpose. The developed algorithm has been tested on variant NLEOV problems and found the convergence is better than bracketing iterative method (BIM), which does not observe any pitfall and is almost equivalent to NRIM.

Keywords:

Numerical hybrid iterative technique,Nonlinear equations in one variable,Bracketing iterative method,Newton Raphson's iterative method,Taylor series method,

Refference:

I. A. Sidi, “Unified treatment of regula falsi, Newton–Raphson, secant, and Steffensen methods for nonlinear equations, J,” Online Math. Appl, vol. 6, 2006.
II. Sanaullah Jamali1, Zubair Ahmed Kalhoro, Abdul Wasim Shaikh, Muhammad Saleem Chandio. ‘AN ITERATIVE, BRACKETING & DERIVATIVE-FREE METHOD FOR NUMERICAL SOLUTION OF NON-LINEAR EQUATIONS USING STIRLING INTERPOLATION TECHNIQUE’. J. Mech. Cont. & Math. Sci., Vol.-16, No.-6, June (2021) pp 13-27. DOI : 10.26782/jmcms.2021.06.00002.
III. D. Babajee and M. Dauhoo, ‘An analysis of the properties of the variants of Newton’s method with third order convergence,’ Applied Mathematics and Computation, vol. 183, no. 1, pp. 659–684, 2006.
IV. J. Kou, Y. Li, and X. Wang, ‘On modified Newton methods with cubic convergence,’ Applied Mathematics and Computation, vol. 176, no. 1, pp. 123–127, 2006.
V. M. A. Noor and F. Ahmad, ‘Numerical comparison of iterative methods for solving nonlinear equations,’ Applied mathematics and computation, vol. 180, no. 1, pp. 167–172, 2006.
VI. M. A. Noor, F. Ahmad, and S. Javeed, ‘Two-step iterative methods for nonlinear equations,’ Applied Mathematics and Computation, vol. 181, no. 2, pp. 1068–1075, 2006.
VII. M. Allame and N. Azad, “On Modified Newton Method for Solving a Nonlinear Algebraic Equations by Mid-Point,” World Applied Sciences Journal, vol. 17, no. 12, pp. 1546–1548, 2012.
VIII. M. Frontini and E. Sormani, “Modified Newton’s method with third-order convergence and multiple roots,” Journal of computational and applied mathematics, vol. 156, no. 2, pp. 345–354, 2003.
IX. M. Frontini and E. Sormani, “Third-order methods from quadrature formulae for solving systems of nonlinear equations,” Applied Mathematics and Computation, vol. 149, no. 3, pp. 771–782, 2004.
X. M. M. Moheuddin, M. J. Uddin, and M. Kowsher, “A new study to find out the best computational method for solving the nonlinear equation”.
XI. N. Bićanić and K. Johnson, “Who was ‘–Raphson’?,” International Journal for Numerical Methods in Engineering, vol. 14, no. 1, pp. 148–152, 1979.
XII. N. Ujević, “A method for solving nonlinear equations,” Applied mathematics and computation, vol. 174, no. 2, pp. 1416–1426, 2006.
XIII. O. C. Ebelechukwu, B. O. Johnson, A. I. Michael, and A. T. Fidelis, “Comparison of Some Iterative Methods of Solving Nonlinear Equations,” International Journal of Theoretical and Applied Mathematics, vol. 4, no. 2, p. 22, 2018.
XIV. R. B. J. Faires, Numerical Analysis 2001 PWS Publishing Company Boston. USA.
XV. S. Abbasbandy, “Improving Newton–Raphson method for nonlinear equations by modified Adomian decomposition method,” Applied Mathematics and Computation, vol. 145, no. 2–3, pp. 887–893, 2003.
XVI. Sanaullah Jamali1, Zubair Ahmed Kalhoro, Abdul Wasim Shaikh, Muhammad Saleem Chandio. ‘AN ITERATIVE, BRACKETING & DERIVATIVE-FREE METHOD FOR NUMERICAL SOLUTION OF NON-LINEAR EQUATIONS USING STIRLING INTERPOLATION TECHNIQUE’. J. Mech. Cont. & Math. Sci., Vol.-16, No.-6, June (2021) pp 13-27. DOI : 10.26782/jmcms.2021.06.00002.
XVII. T. Yamamoto, “Historical developments in convergence analysis for Newton’s and Newton-like methods,” Journal of Computational and Applied Mathematics, vol. 124, no. 1–2, pp. 1–23, 2000.
XVIII. V. Kanwar, V. Kukreja, S. Singh, and others, “On a class of quadratically convergent iteration formulae,” Applied mathematics and Computation, vol. 166, no. 3, pp. 633–637, 2005.
XIX. V. Kanwar, V. Kukreja, S. Singh, and others, “On some third-order iterative methods for solving nonlinear equations,” Applied Mathematics and Computation, vol. 171, no. 1, pp. 272–280, 2005.
XX. X. Wu and D. Fu, “New high-order convergence iteration methods without employing derivatives for solving nonlinear equations,” Computers & Mathematics with Applications, vol. 41, no. 3–4, pp. 489–495, 2001.

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Abstract:

This study proposed a correlation analysis of two recent approaches. The FAST ICA technique is used for the separation of the multimodal data (i.e, mixture of audio, noise and image signal) and the minimum mean-square error (MMSE) is used for the removal of white noise from the audio signal. Initially, multimodal data will be formed by combining all the three signals (i.e. a mixture of audio, noise and image signals). For creating an ideal situation and for SNR comparisons, separation of the signals will be performed using the Fast ICA technique. ICA, Independent element analysis is a recently developed technique in which the goal is to seek a linear interpretation of non-Gaussian knowledge for the elements to be as statistically free as possible. Such representations record the key structure of the data in several applications, including signal quality and signal separation. ICA learns a linear decay of the data. ICA can find the basic elements and sources included in the data found where traditional methods fail. After the separation of the mixed data, denoising will be performed using the MMSE technique. The main purpose of the MMSE technique is to remove White Noise from the unmixed audio signal which will be further used for overall and segmental SNR comparisons for quality enhancement. Based on the designed algorithms, both of these techniques are real-time data-driven programs. These techniques are explored with standard De-noising methods using several different estimation methods like signal-to-noise ratio (SNR). Experimental results prove that the proposed MMSE technique works well for both noise segmentation and overall consideration of noise distortion signals. These statistical techniques can be used in many applications, such as in different communication systems to eliminate background noise and in channels to reduce channel interference between different applications in speech communications

Keywords:

Minimum Mean Square Error (MMSE),Filtering and Thresholding Techniques,Additive White Gaussian Noise (AWGN),Signal-to-Noise Ratio (SNR),Fast ICA,Whitening,Centering,

Refference:

I. A Rapid Match Algorithm for Continuous Speech Recognition by Laurence S. Gillick and Robert Roth Dragon Systems, Inc. 90 Bridge St. Newton MA. 02158.
II. A. Ravi, Leela Satyanarayana. V., : GREY WOLF OPTIMIZATION WITH WAVELET SCHEME FOR SAR IMAGES DENOISING. J. Mech. Cont.& Math. Sci., Vol.-14, No.-5, September-October (2019) pp 558-570. DOI : 10.26782/jmcms.2019.10.00040.
III. Blind Separation of Sources: A Non-Linear Neural Algorithm Gilles BUREL (1992).
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Abstract:

Significant revolution in different organizations chief’s point of view toward customer treating and the level of product presentation or services resulted in redefining the structure of these organizations based on this point of view. The municipal services are very important as well. The strategy of “CRM” which was so successful in the private sector and has been applying as “CiRM” in the public sector of developed countries could be very useful for this achievement. The main goal of citizen management is realizing the citizen's needs and demands, improving communication through connection with citizens and optimizing it to increase the level of their satisfaction. The government agencies do it based on their idea and point of view cause the citizen are valuable assets in the planning of services and reduction of costs. This study proposes a combined data mining method to discover hidden knowledge in call citizen compliant of the municipality of Tehran. A Self-organizing map neural network was used to identifying and classifying citizen needs based on RFM analysis. It also classified citizen needs into three majors. the result of classification and clustering of SOM has created a new feature to profiled call’s customer to identify temporal-spatial patterns of problems by using an association rule with the Apriori algorithm. The results of this idea demonstrate that accordance of citizens call compliant in a different area and discovering hidden knowledge can facilitate the performance of human recourse in improving services to citizens.

Keywords:

citizen management,data mining,RFM-SOM algorithm,Apriori algorithm,a new feature ,

Refference:

I. Anthony Danna & Oscar H. Gandy Jr. (2000) .All That Glitters is Not Gold: Digging Beneath the Surface of Data mining. Journal of Business Eithics 373-386.
II. Ahmadvand .A (2010)hybrid data mining model for effective citizen relationship management : a case study on theran municipalit, International Conference on e-ducation.e-business.e-management and learning. Iran
III. Akhondzadeh-Noughabi.E. (2013). FTiS:A new model for effective urban management : A case study of urban systems in iran. Cities, pp.394-403.
IV. Akhondzadeh-Noughabi.E, Amin-Naseri, A. Albadvi. And Saeedi. M (2016). Human resource performance evaluation from CRM perspective: a two-step association rule analysis. Int. J. Business Performance Management, 17. 1
V. Agrawal .Rand. Srikant.R (1994) ‘Fast algorithms for mining association rules’, Proc. 20th Int. Conf. Very Large Data Bases, VLDB, pp.487–499

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VIII. Ghodousi, M, Alesheikh, A, Saeidian, B. Pradhan and G. Lee. (2019). Evaluating Citizen Satisfaction and Prioritizing Their Needs Based on Citizens’ Complaint Data Sustainability 2019, 11, 459.
IX. Ching Z. X. (2004,). Mining class outliers: concepts ,algorithms and applications in CRM ,. Expert systems with Applications 681-69
X. Han. J and kamber. A (2001) Data Mining: Concepts and Techniques, p.5, Morgan Kaufmann, San Francisco, CA
XI. Hughes. A.M (1994). Strategic database marketing. Chicago: Probus Publishing Company
XII. J. Dunn. : Well separated clusters and optimal fuzzy partitions. 4. 95-104. 1974
XIII. H. H. Chen, Wud. (2013). Customer relationship management in the hairdressing industry: An application of data mining techniques Expert Systems with Applications 40 ,7513–7518
XIV. R. Liu. D,(2005). Integrating AHP and data mining for product recommomerendation based on customer lifetime value ,. information and Management 42, 340-387.
XV. Hsieh, N.C. (2004.) An integrated data mining and behavioral scoring model for analyzing bank customers, Expert Systems with Applications 27 623–633
XVI. Redick C. GR (2004). A two- stage model of e-government growth: Theories and empiirical evidence for U.S cities,. Government Information Quarterly, 21:51-64.
XVII. Reddick C. G.(2009). The aoption of centralized customer service systems: A survey of local governments,. Goverment Information Quarte 26: -226
XVIII. Schellong. A. L. (2007).Managing citizen relationships in disasters :. proceedings of the 40th annual Hawaii international conference on system sciences. Hurricane Wilma,311 and Miami-Dade country.
XIX. Schellong. A. (2005 (CRM in the public sector: towords a conceptual research framework. national conferance on digital government research. Atlanta,Georgia,.
XX. Silva.R. (2007) Boosting goverment reputation through CRM. The international journal of public Sector Management, (7):588-6.
XXI. Sasaki.Takanori A. (2007) An Empirical study on citizen Relationship Management in japan,.
XXII. Srinivas D., K. Rajkumar, N. Hanumantha Rao. : ‘ SERVICE QUALITY DIMENSIONS-A STUDY OF SELECT PUBLIC AND PRIVATE SECTOR BANKS OF WARANGAL DISTRICT’. J. Mech. Cont. & Math. Sci., Vol.-15, No.-8, August (2020) pp 307-314. DOI : 10.26782/jmcms.2020.08.00029.
XXIII. Tan, P.N. Steinbach M.and. Kumar (2006) Introduction to Data Mining, Pearson Education Inc., US
XXIV. Taniar. D (2008) Data Mining and Knowledge Discovery Technologies, IGI Global, New York.

XXV. Vellido, A. Lisboa, P. J. G., & Vaughan. J (1999). neural networks in business: a survey of applications (1992–1998). Expert Systems with Applications, 17, 51–70.

XXVI. Zayyanu Umar, Agozie Eneh, Okereke George E. : ‘JOINED HETEROGENEOUS CLOUDS RESOURCES MANAGEMENT: AN ALGORITHM DESIGN’. J. Mech. Cont.& Math. Sci., Vol.-15, No.-8, August (2020) pp 39-52. DOI: 10.26782/jmcms.2020.08.00005

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Abstract:

In this article, we would like to introduce some new types of convex function, which we named quasi convex function and convex function. With the help of these new notions we would also state the well-known Hermite Hadamard dual inequalities which we call Hermite Hadamard dual inequality for quasi convex function and convex function, respectively. In this way various new results related to Hermite Hadamard inequalities would be obtained and some would be captured as special cases by varying different values of .

Keywords:

Hermite–Hadamard dual inequality,p–convex function,quasi-convex function,P–convex function,

Refference:

I. Ambreen Arshad and Asif Raza Khan, Hermite-Hadamard-Fejer type inequalities for s-p-convex functions of several senses, TJMM, 11 (2019), 25–40.
II. Asif Raza Khan, Inam Ullah Khan and Siraj Muhammad, Hermite-Hadamard type fractional inequalities for s-convex functions of mixed kind, TMCS, 1 (2021), 25–37.
III. Charles Hermite, Sur deux limites d’une inte ́grale de ́finie, Mathesis, 3 (1883), 82.
IV. Edwin Ford Beckenback, Convex Functions, Bull. Amer. Math. Soc., 54 (1948), 439–460.
V. I ̇mdat Iscan, Hermite-Hadamard and Simpson like inequalities for differentiable harmonically convex functions, J. Math., 2014, (2014), Article 346305.
VI. I ̇mdat Iscan, Hermite-Hadamard type inequality for p-convex functions, Int. J. Anal. App., 11(2), (2016), 137–145.
VII. I ̇mdat Iscan, Selim Nauman and Kerim Bekar, Hermaite-Hadamard and Simpson type inequalities for differentiable harmonically P-convex functions, British. J. Math. & Comp., 4 (14) (2014), 1908–1920.
VIII. Jaekeun Park, Hermite-Hadamard and Simpson-like type inequalities for differentiable Harmonically Quasi-convex functions, Int. J. Math. Anal., 8(33), (2014), 1692–1645.
IX. Mehmet Kunt and I ̇mdat Iscan, Hermite-Hadamard-Fejer type inequalities for p-convex functions, Arab. J. Math., 23(1), (2017), 215–230.
X. Muhammad Aslam Noor, Khalida Inayat Noor, Marcela Mihai and Muhammad Uzair Awan, Hermite-Hadamard inequalities for differentiable p-convex functions using hypergeometric functions, Publications de L’Institut Mathematique, 100(114), (2015), 251–257.
XI. Muhammad Bilal and Asif Raza Khan, New Generalized Hermite-Hadamard Inequalities for p-convex functions in the mixed kind, EJPAM (Accepted), 2021.
XII. Muhammad Bilal, Muhammad Imtiaz Asif Raza Khan, Ihsan Ullah Khan and Muhammad Zafran, Generalized Hermite-Hadamard inequalities for s-convex functions in mixed kind, (Submitted), (2021).
XIII. Murat Emin O ̈zdemi ́r, Merve Avci and Havva Kavurmaci, Hermaite-Hadamard type inequalities via (α, m) convexity, Comp. Math. App., 61, (2011), 2614–2620.
XIV. Mehmood Faraz, Asif R. Khan, & M. Azeem Ullah Siddique. : ‘SOME RESULTS RELATED TO CONVEXIFIABLE FUNCTIONS’. J. Mech. Cont.& Math. Sci., Vol.-15, No.-12, December (2020) pp 36-45. DOI : 10.26782/jmcms.2020.12.00004
XV. Silvestru Sever Dragomir, Josip Pec ̌aric ́ and Lars-Erik Persson, Some inequalities of Hadamard type, Soochow J. Math., 21(3) (1995), 335–341.

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Abstract:

The intuitionistic fuzzy entropy (IFE) is being used to measure the degree of uncertainty of a fuzzy set (FS) with alarming accuracy and precision more accurately than the fuzzy set theory. Entropy plays a very important role in managing the complex issues efficiently which we often face in our daily life. In this paper, we first review several existing entropy measures of intuitionistic fuzzy sets (IFSs) and then suggest two new entropies of IFSs better than the existing ones. To show the efficiency of proposed entropy measures over existing ones, we conduct a numerical comparison analysis. Our entropy methods are not only showing better performance but also handle those IFSs amicably which the existing method fails to manage.  To show the practical applicability and reliability, we utilize our methods to build intuitionistic fuzzy Portuguese of interactive and multicriteria decision making      (IF-TODIM) method. The numerical results show that the suggested entropies are convenient and reasonable in handling vague and ambiguous information close to daily life matters.

Keywords:

Intuitionistic Fuzzy Sets,Entropy Measure,Multicriteria Decision Making,IF-TODIM,

Refference:

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III. Burillo, P., & Bustince, H. (1996). Entropy on intuitionistic fuzzy sets and on interval-valued fuzzy sets. Fuzzy sets and systems, 78(3), 305-316.
IV. Bustince, H., & Burillo, P. (1996). Vague sets are intuitionistic fuzzy sets. Fuzzy sets and systems, 79(3), 403-405.
V. De Luca, A., & Termini, S. (1972). A definition of a non probabilistic entropy in the setting of fuzzy sets theory. Information and control, 20(4), 301-312.
VI. De, S. K., Biswas, R., & Roy, A. R. (2000). Some operations on intuitionistic fuzzy sets. Fuzzy sets and Systems, 114(3), 477-484.
VII. Fan, J. L., & Ma, Y. L. (2002). Some new fuzzy entropy formulas. Fuzzy sets and Systems, 128(2), 277-284.
VIII. Gau, W. L., & Buehrer, D. J. (1993). Vague sets. IEEE transactions on systems, man, and cybernetics, 23(2), 610-614.
IX. Huwang, K. & Yang, M. S. (2008). On entropy of fuzzy set. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 16(4), 519-527.
X. Kahneman, D. & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47, 263-292.
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XIII. Li, L. (2016). A new entropy-based intuitionistic fuzzy multi-attribute decision making method. American Journal of Applied Mathematics, 4(6), 277-282.
XIV. Liu, M., & Ren, H. (2014). A new intuitionistic fuzzy entropy and application in multi-attribute decision making. Information, 5(4), 587-601.
XV. Mishra, R. (2016). Intuitionistic fuzzy information measures with application in rating of township development, Iranian Journal of Fuzzy Systems, 13, 49-79
XVI. Pal, N. R., & Pal, S. K. (1989). Object-background segmentation using new definitions of entropy. IEE Proceedings E (Computers and Digital Techniques), 136(4), 284-295.
XVII. Razia Sharif, Zahid Hussain, Shahid Hussain, Sahar Abbas, Iftikhar Hussain, ‘A NOVEL FUZZY ENTROPY MEASURE AND ITS APPLICATION IN COVID-19 WITH FUZZY TOPSIS’. J. Mech. Cont. & Math. Sci., Vol.-16, No.-6, June (2021) pp 52-63. DOI : 10.26782/jmcms.2021.06.00005.
XVIII. Rani, P., Jain, D., & Hooda, D. S. (2019). Extension of intuitionistic fuzzy TODIM technique for multi-criteria decision making method based on shapley weighted divergence measure. Granular Computing, 4(3), 407-420.
XIX. Siddique, M. (2009). Fuzzy decision making using max-min and MMR methods.
XX. Szmidt, E., & Kacprzyk, J. (2001). Entropy for intuitionistic fuzzy sets. Fuzzy sets and systems, 118(3), 467-477.
XXI. Tsallis, C. (2019). Beyond Boltzmann–Gibbs–Shannon in physics and elsewhere. Entropy, 21(7), 696.
XXII. Verma, R., & Sharma, B. D. (2011). On generalized exponential fuzzy entropy. World Academy of Science, Engineering and Technology, 60, 1402-1405.
XXIII. Wang, J. Q., & Wang, P. (2012). Intuitionistic linguistic fuzzy multi-criteria decision-making method based on intuitionistic fuzzy entropy. Control and decision, 27(11), 1694-1698.
XXIV. Wei, C. P., Gao, Z. H., & Guo, T. T. (2012). An intuitionistic fuzzy entropy measure based on trigonometric function. Control and Decision, 27(4), 571-574.
XXV. Yager, R. R. (1979). On the measure of fuzziness and negation part I: membership in the unit interval.
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XXVIII. Zahid Hussain, Sahar Abbas, Shahid Hussain, Zaigham Ali, Gul Jabeen. : ‘SIMILARITY MEASURES OF PYTHAGOREAN FUZZY SETS WITH APPLICATIONS TO PATTERN RECOGNITION AND MULTICRITERIA DECISION MAKING WITH PYTHAGOREAN TOPSIS’. J. Mech. Cont. & Math. Sci., Vol.-16, No.-6, June (2021) pp 64-86. DOI : 10.26782/jmcms.2021.06.00006.
XXIX. Zeng, W., & Li, H. (2006). Relationship between similarity measure and entropy of interval valued fuzzy sets. Fuzzy sets and Systems, 157(11), 1477-1484.
XXX. Zhang, Q. S., & Jiang, S. Y. (2008). A note on information entropy measures for vague sets and its applications. Information Sciences, 178(21), 4184-4191.

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Abstract:

In this research, a unique textile antenna is reported for ultra-wideband applications. The material used for the ground and patch of an antenna is conductive woven zelt and the substrate of the antenna is made of cotton (Tan δ = 0.02, εr = 1.54). The suggested antenna is made of a circular patch of a miniature size i.e. 20 mm × 16.922 mm × 2 mm. The zelt is 0.03 mm thick, bearing electrical conductivity up to 0.01 Ω/m. The antenna bandwidth and gain are optimized by using a multi-objective evolutionary algorithm based on decomposition with differential evolution (MOEA/D-DE). The gain and bandwidth are improved to 4.9 dBi and 2.8 GHz to 15 GHz, respectively. The suggested antenna can be used for Wifi, GPS, and ultra-wideband operations.

Keywords:

Antenna,genetic algorithms,optimization,simulations,ultra-wideband,

Refference:

I. A. Wu, Z. Zhang, and B. Guan, “Wideband printed antenna design using a shape blending algorithm,” International Journal of Antennas and Propagation, vol. 2017, 2017.
II. B. Tian, M. Deng, and Z. Li, “Time domain optimization of UWB antenna by means of genetic algorithm,” in 2008 8th International Symposium on Antennas, Propagation and EM Theory, 2008, pp. 883-886.
III. C.-L. Hwang and A. S. M. Masud, Multiple objective decision making—methods and applications: a state-of-the-art survey vol. 164: Springer Science & Business Media, 2012.
IV. C. Yu, T. Xu, and C. Liu, “Design of a novel UWB omnidirectional antenna using particle swarm optimization,” International Journal of Antennas and Propagation, vol. 2015, 2015.
V. D. A. Van Veldhuizen and G. B. Lamont, “Multiobjective evolutionary algorithms: Analyzing the state-of-the-art,” Evolutionary computation, vol. 8, pp. 125-147, 2000.
VI. H. Li and Q. Zhang, “Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II,” IEEE transactions on evolutionary computation, vol. 13, pp. 284-302, 2008.
VII. K. Deb and R. B. Agrawal, “Simulated binary crossover for continuous search space,” Complex systems, vol. 9, pp. 115-148, 1995.
VIII. K. Mahmoud, “UWB antenna design using gravitational search algorithm,” JES. Journal of Engineering Sciences, vol. 41, pp. 1890-1903, 2013.
IX. K. Price, R. M. Storn, and J. A. Lampinen, Differential evolution: a practical approach to global optimization: Springer Science & Business Media, 2006.
X. M. Karimiyan-Mohammadabadi, M. Dorostkar, F. Shokuohi, M. Shanbeh, and A. Torkan, “Ultra-wideband textile antenna with circular polarization for GPS applications and wireless body area networks,” Journal of industrial textiles, vol. 46, pp. 1684-1697, 2017.
XI. M. C. Derbal, A. Zeghdoud, and M. Nedil, “A Dual Band Notched UWB Antenna with Optimized DGS Using Genetic Algorithm,” Progress In Electromagnetics Research, vol. 88, pp. 89-95, 2020.
XII. M. T. Asghar, M. F. Shafique, I. Usman, N. Gogosh, and M. A. Khan, “Design and Optimization of an UWB Antenna with 5.8 GHz Band Suppression Using Genetic Algorithm,” Journal of Basic and Applied, vol. 3, pp. 701-707, 2013.
XIII. NU Khan, FU Khan, “RF Energy Harvesting for Portable Biomedical Devices” 22nd International Multitopic Conference (INMIC), 2019.
XIV Q. Zhang, A. Zhou, S. Zhao, P. N. Suganthan, W. Liu, and S. Tiwari, “Multiobjective optimization test instances for the CEC 2009 special session and competition,” 2008.
XV. Q. Zhang and H. Li, “MOEA/D: A multiobjective evolutionary algorithm based on decomposition,” IEEE Transactions on evolutionary computation, vol. 11, pp. 712-731, 2007.
XVI. Q. Zhang, W. Liu, and H. Li, “The performance of a new version of MOEA/D on CEC09 unconstrained MOP test instances,” in 2009 IEEE congress on evolutionary computation, 2009, pp. 203-208.
XVII. R. Storn and K. Price, “Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces,” Journal of global optimization, vol. 11, pp. 341-359, 1997.
XVIII . Y.-L. Li, W. Shao, L. You, and B.-Z. Wang, “An improved PSO algorithm and its application to UWB antenna design,” IEEE Antennas and wireless propagation letters, vol. 12, pp. 1236-1239, 2013.
XIX. Zanjani Payam Shojaeian, Saeed Ebrahimi Nejad Motlagh Tehrani. : ‘DETECTION OF ABNORMAL BEHAVIOR OF THE SYSTEM AND INCREASE THE SECURITY OF CLOUD COMPUTING BASED ON EVOLUTIONARY ALGORITHM’. J. Mech. Cont.& Math. Sci., Vol.-14, No.-6 November-December (2019) pp 205-225. DOI. : 10.26782/jmcms.2019.12.00016

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