Archive

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).
IV. D. Marr; E. Hildreth Proceedings of the Royal Society of London. Series B, Biological Sciences, Vol. 207, No. 1167. (Feb. 29, 1980), pp. 187-217
V. Elad m. 2002 ways to improve bilateral filter IEEE trans on image processing 11(10) pg 1141-1151.
VI. E. Abreu, M. Lightstone, S. K. Mitra, and K. Arakawa, A new efficient approach for the removal of white noise from highly corrupted images, IEEE Transactions on Image Processing, vol. 5, no. 6, pp. 10121025, 1996.
VII. Gonzalez, Woods, and Richard E. Woods. ”Eddins, Digital Image Processing Using MATLAB.” Third New Jersey: Prentice Hall (2004).
VIII. International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-2, Issue-5, June 2013.
IX. Independent component analysis, A new concept Pierre Comon (1994).
X. Image Analysis and Mathematical Morphology by Jean Serra, ISBN 0-12-637240-3 (1982).
XI. International Journal on Recent and Innovation Trends in Computing and Communication, ISSN: 2321-8169 Volume: 2 Issue: 6 1657 – 1661.
XII. J Portilla, V Strela, M Wainwright, and E P Simon celli, “Image denoising using scale mixtures of Gaussians in the wavelet domain,” IEEE Trans. Image Processing., In Press. 2003.
XIII. L. Rudin, S. Osher, E. Fatemi Nonlinear total variation based noise removal algorithms Physica D, 60 (1992), pp. 259–268.
XIV. Michael, Weeks. ”Digital Signal Processing Using MATLAB .”Pearsonpublications, ISBN81-297-0272-X 2.13 (2011): 15-16.
XV. Motwani M. C., Gadiya M. C., Motwani R. C. and Jr. Harris F. C. (2004), Survey of image denoising techniques, Proceedings of Global Signal Processing Expo andConference (GSPx 04),, Santa Clara, CA, USA.
XVI. Mallat, S., 1989. A theory for multiresolution signal decomposition: The wavelet representation. IEEE transactions on pattern analysis and machine intelligence 11, 674–693.
XVII. P. Mrazek, J. Weickert, and A. Bruhn. Geometric Properties from Incomplete Data, chapter On Robust Estimation and Smoothing with Spatial and Tonal Kernels. Springer, 2006.
XVIII. P. Prandoni, “Optimal Segmentation Techniques for Piecewise Stationary Signals,” Ph.D. thesis, Ecole Polytechnique Federale de Lausanne, Switzerland (1999).
XIX. PietroPerona and Jitendra Malik (July 1990). “Scale-space and edge detection using anisotropic diffusion” IEEE Transactions on Pattern Analysis and Machine Intelligence pg 629–639.
XX. Rafael C. Gonzalez, Richard E. Woods, Steven L. Eddins. Digital Image Processing Using MATLAB [M]. McGraw-Hill Press, 2011.
XXI. Scott E Umbaugh, Computer Vision and Image Processing, Prentice Hall PTR, New Jersey, 1998.
XXII. S. H. Chung and R. A. Kennedy, “Forward-Backward Nonlinear Filtering Signals from Noise, “Journal of Neuroscience Methods, vol. 40, pp. 71-86, 1991.
XXIII. Utpal Barman, Ridip Dev Choudhury. : ‘Prediction of Soil pH using Smartphone based Digital Image Processing and Prediction Algorithm’. J.Mech.Cont.& Math. Sci., Vol.-14, No.2, March-April (2019) pp 226-249. DOI : 10.26782/jmcms.2019.04.00019.
XXIV. Wayne Niblack, An Introduction to Image Processing, Prentice-Hall, NewJersey, 1986.filter, IEEE Xplore PDCAT pp.826-828.

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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

VI. Buckinxa,W.(2004). Customer-adapted coupon targeting using feature selection, . Expert Systems with Application,pp 509-518
VII. Chang L,Che-Wei(2009). Mining the text information to optimizing the customer relationship management,. Expet systems with Applications 1443 -1433.
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:

I. Atanassov, K. T. (1999). Intuitionistic fuzzy sets. In Intuitionistic fuzzy sets, 1-137.
II. Bhandari, D., & Pal, N. R. (1993). Some new information measures for fuzzy sets. Information Sciences, 67(3), 209-228.
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.
XI. Korner, S. (1967). Laws of thought. Encyclopedia of philosophy, 4, 414-417.
XII. Lejewski, C. (1967). “Jan Lukasiewicz,” Encyclopedia of Philosophy, 5, 104-107.
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.
XXVI. Zadeh, L.A. (1965). “Fuzzy sets,” Info. & Ctl. 8, 338-353.
XXVII. Zadeh, L.A. (1968). Probability measures of fuzzy events. J. Math. Anal. Appl, 23, 421–427.
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.
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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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Abstract:

Alternate energy sources such as hybrid renewable energy off-grid systems are under the focus of researchers to improve their reliability and feasibility for rural areas. A hybrid power system uses a combination of renewable as primary and fuel-based power systems as a backup. Reliability, affordability, and cost depend upon the number of power systems used and the efficiency of these systems. However, the hybrid system is facing different challenges such as high cost, fluctuations in power, and proper infrastructure. This study aimed to determine the best configuration for village Bakhar Jamali, having a total of 162 houses and a 380 kW peak load. This study has been carried out using HOMER Pros to check the different sets of hybrid configurations. To find optimal power different sets of schemes were carried out. It was concluded in this study that the combination of Wind turbine, Solar PV, Biogas Generator, Diesel generator, Battery, and Converter give the optimum hybrid system with the following rated capacity, 150 kW of Solar PV, Specification of 3 kW of 50 Wind Turbine, Auto size Diesel Generator of 420 kW, Biogas Generator of 150 kW, Number of Batteries of 1 kWh 3832 and Converter capacity of 470 kW.

Keywords:

Hybrid,HOMER Pro,Grid system,Reliabilit,Optimum,Configurations,Wind,Solar,

Refference:

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II. Ali, M., Ahmed, A. B., Ullah, K., & Khan, A. (2020, February). Multiple-Criteria Policy Anaysis of Circular Debt in Pakistan. In 2020 International Conference on Engineering and Emerging Technologies (ICEET) (pp. 1-7). IEEE.
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IV. 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.
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VII. Halima, A., Fudholia, A., Kamaruzzaman Sopiana, M. H. R., & Phillipsb, S. J. (2018). Feasibility Study on Hybrid Solar Photovoltaic with Diesel Generator and Battery Storage Design and Sizing Using HOMER Pro®. Jurnal Kejuruteraan SI, 1(3), 69-76.
VIII. Ilyas, R. (2021). Energy consumption: The importance of institutional quality in Pakistan. Journal of Applied Economics and Business Studies, 5(1), 143-174.
IX. Khan, F. A., Pal, N., & Saeed, S. H. (2018). Review of solar photovoltaic and wind hybrid energy systems for sizing strategies optimization techniques and cost analysis methodologies. Renewable and Sustainable Energy Reviews, 92, 937-947.
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XII. Kumar, N. M., Chopra, S. S., Chand, A. A., Elavarasan, R. M., & Shafiullah, G. M. (2020). Hybrid renewable energy microgrid for a residential community: A techno-economic and environmental perspective in the context of the SDG7. Sustainability, 12(10), 3944.
XIII. Lee, H. J., Vu, B. H., Zafar, R., Hwang, S. W., & Chung, I. Y. (2021). Design Framework of a Stand-Alone Microgrid Considering Power System Performance and Economic Efficiency. Energies, 14(2), 457.
XIV. M. Sai Krishna Reddy, D. Elangovan : RURAL ELECTRIFICATION WITH RENEWABLE ENERGY FED DC MICRO GRID. J. Mech. Cont.& Math. Sci., Vol.-15, No.-8, August (2020) pp 25-38. DOI : 10.26782/jmcms.2020.08.00004.
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XVI. Mehrjerdi, H. (2020). Modeling, integration, and optimal selection of the turbine technology in the hybrid wind-photovoltaic renewable energy system design. Energy Conversion and Management, 205, 112350.
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Abstract:

Grassmannian bi-complex contains two types of differential maps  and . This complex is related to the Tangent complex by Siddiqui for the differential map. In this article, we try to find morphisms in tangential configuration space to relate Grassmannian complex and first-order tangent complex for differential map d'.

Keywords:

Grassmannian complex,Configuration,Vector Space,Cross-Ratio,Tangent Complex,

Refference:

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Abstract:

Small and Medium Enterprises (SMEs) are the foundation of every major economy in the world. The majority of these industries are fighting for survival in a hostile climate. In the SMEs sector, the Lean models have been implemented with an emphasis on economic efficiency. The various Lean Models are used in SMEs as well as in large Industries. The Lean Models are considered for the improvement of company performance which includes production, productivity, inventory, raw material, quality, and customer satisfaction. therefore, in this research work which lean models are being implemented in SMEs of Sindh was investigated. The survey questionnaires were distributed amongst 70 SMEs of  Sindh based on Six Sigma, 5S, Green Manufacturing, Kaizen, Poka Yoke, TPM, TQM, SCM, Standardize of Work lean models. The results conclude that 5's and Standardize of work are mostly implemented about 87.5% & 72.9% as compared to the other models. whereas PokaYoke Model and Total Product Management Model are considered as least implemented the model in SMEs with 18.6% and 10%. Moreover, in terms of location, Hyderabad seems highly impacted region in Lean Model Implementation

Keywords:

Manufacturing efficiency,Lean manufacturing models,Small and medium enterprises,Six Sigma,Poka Yoke,Total Productive Maintenance,

Refference:

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