iosr-Jm   Volume-22 ~ Issue-2 ~ Mar. – Apr. 2026

Abstract : The primary aim of this study is to design an encryption–decryption algorithm that integrates Bhāskara’s cyclic technique, XOR operations, and LU factorization of a non-singular matrix for structured key generation. A comprehensive numerical illustration is provided to verify the accuracy, reversibility, and practical applicability of the proposed scheme. This research demonstrates a constructive synthesis between traditional Indian mathematical knowledge systems (IKS) and contemporary lightweight cryptographic methodologies.

Keywords: Bhāskara’s Cyclic Technique, XOR Logic, LU decomposition of a Matrix, Cryptography

[1]. Chandulal A.: Some Introduction To Bhaskara – II (1114-1185), International Journal Of Multidisciplinary Educational Research, ISSN:2277-7881, Volume:13, Issue:6(1), June: 2024, Pp. 158-165.
[2]. Dixit Sandeep, Dobahl Girish, Pandey Shweta: Encrypt And Decrypt Messages Based On LU Decomposition Using Multiple Keys, International Journal Of Scientific & Technology Research, ISSN 2277-8616, Vol. 8, Issue 11, November 2019 Pp. 3347-3351.
[3]. Forouzan Behrouz A: Cryptography & Network Security, Mcgraw Hill Education, 2007.
[4]. Garg Satish Kumar: Cryptography Using XOR Cipher, Research Journal Of Science And Technology, ISSN 0975-4393 (Print), Vol. 09, Issue-01, January -March 2017.
[5]. Kahate Atul: Cryptography And Network Security, Tata Mcgraw Hill, New Delhi, 2008.

Abstract : This study proposes a novel EOQ-based inventory model for deteriorating items under seasonal demand Variation, inflation, and investment in preservation technology Demand is modeled time- dependent Seasonal function while deterioration is treated as controllable through preservation efforts, Creating a cost-benefit trade-off between technology investment and spoilage reduction. The time value of money and inflation are incorporated using a discounted Cash flow approach to reflect realistic economic condition. A Comprehensive total Cost function is formulated including Ordering, holding, deterioration, preservation investment and inflation-adjusted costs.......

Keywords: Perishable inventory System, Seasonal demand, Preservation technology optimization, Inflation-adjusted costs, EOQ model, Deterioration control.

[1]. Abad, P.L. (2000): Optimal Lot Size For A Perishable Good Under Condition Of Neither Production And Partial Backordering And Lost Sale. Computer & Industrial Engineering, Pp457-465
[2]. Buzacott (1975): Deteriorating Inventory Model With Variable Holding Cost And Price Dependent Time Varying Demand. Operational Research Quarterly, 26(3), 553-558
[3]. Covert, R.P. & Philip. GC. (1973). An EOQ Model For Items With Weibull Distribution Deterioration. Alle Transactions, 5(4), 323-326
[4]. Donaldson, W.A (1977). Inuentory Replenishment Policy For A Linear Bend In Demand: An Analytical Solution. Operational Research Quarterly, 28(3), 663-670
[5]. Dye, CY. And Ouyang, L.Y (2005): An EOQ Model For Perishable Item Under Stock Dependent Selling Rate And Time-Dependent Partial Backlogging. European Journal Of Operation Research, 163, PP776-783

Abstract : In this paper study develops an inventory model for deteriorating items under a realistic market environment characterized by time-dependent demand, time-varying holding cost, and inflation. Traditional Economic Order Quantity models assume constant demand and holding costs; however, such assumptions are often impractical in dynamic economic conditions. In this study, the demand rate is considered as a function of time, while holding cost.......

Keywords: Economic Order Quantity; Deteriorating Items; Time-Dependent Demand; Time-Varying Holding Cost; Inflation; Optimal Replenishment Policy

[1]. Bhunia, A. K., & Maiti, M. (1998). A two-parameter deteriorating inventory model with time-dependent demand and shortages. International Journal of Systems Science, 29(12), 1325–1333.
[2]. Bhunia, A. K., & Maiti, M. (2000). A deterministic inventory model for deteriorating items with time-dependent demand and inflation. European Journal of Operational Research, 127(3), 505–515.
[3]. De, S. K., & Chaudhuri, K. S. (1986). An EOQ model with time-dependent demand and shortages. International Journal of Systems Science, 17(1), 61–68.
[4]. Dey, B. K., Giri, B. C., & Maiti, M. (2022). A sustainable inventory model for deteriorating items with time-varying demand and inflation. Annals of Operations Research, 315, 987–1008.
[5]. Ghare, P. M., & Schrader, G. F. (1963). A model for exponentially decaying inventory. Journal of Industrial Engineering,14,238–243.

Abstract : In this manuscript, we put forward a modified difference estimator with Exponential Dual to Ratio (EDR) as intercept for estimation of population mean with an aim to study mean square error (MSE)and efficiency of the suggested class of estimator over classical estimators. The proposed model has been discussed along with the numerical illustration. The estimators have higher percent relative efficiency in comparison to the existing estimators.

Keywords: Difference estimator, Auxiliary variable, Exponential dual to ratio, Bias, Mean square error (MSE), Efficiency.

[1]. B. K. Handique, “A Class Of Regression-Cum-Ratio Estimators In Two-Phase Sampling For Utilizing Information From High
Resolution Satellite Data,” Isprs Ann. Photogram. Remote Sens. Spat. Inf. Sci., Vol. I–4, Pp. 71–76, Jul. 2012.
[2]. S. Choudhury And B. K. Singh, “An Efficient Class Of Dual To Product-Cum-Dual To Ratio Estimators Of Finite Population
Mean In Sample Surveys,” Glob. J. Sci. Front. Res., Vol. 12, No. 3-F, 2012.
[3]. H. P. Singh, “Estimation Of Finite Population Mean Using Known Correlation Coefficient Between Auxiliary Characters,”
Statistica, Vol. 65, No. 4, Pp. 407–418, 2005.
[4]. R. Tailor, B Sharma, And J.-M. Kim, “A Generalized Ratio-Cum-Product Estimator Of Finite Population Mean In Stratified
Random Sampling,” Commun Korea Stat Soc, Vol. 18, No. 1, Pp. 111–118, 2011.
[5]. T. Srivenkataramana, “A Dual To Ratio Estimator In Sample Surveys,” Biometrika, Vol. 67, No. 1, Pp. 199–204, 1980.

Abstract : In order to address generalized convex multi-objective optimization problems (GCMOOPs), this work leads a comparable investigation of various algorithmic approaches. Finding the benefits and drawbacks of several strategies, such as slope-based procedures, half-and-half approaches, and evolutionary algorithms, is the main focus. Between a finite layered image space and a genuinely straight topological pre-picture space, we manage multi-objective optimization problems with non-convex constraints and vector-regarded objective functions. The objective functions in question may exhibit part-wise generalized convexity, meaning they can be either semi-rigidly semi convex......

Keywords: Techniques, Generalized Convex, Multi-Objective Optimization, Problems, Algorithmic Approaches

[1]. A. A. Kahn, C. Tammer and C. Z˘alinescu, Set-valued Optimization: An Introduction with Applications, Springer, Berlin, Heidelberg, 2015.
[2]. C. G¨unther and C. Tammer, Relationships between constrained and unconstrained multi-objective optimization and application in location theory, Math Meth Oper Res. 84(2) (2016), 359–387.
[3]. Deb, K., Sindhya, K., & Hakanen, J. (2016). Multi-objective optimization. In Decision sciences (pp. 161-200). CRC Press.
[4]. Elarbi, M., Bechikh, S., Ben Said, L., & Datta, R. (2017). Multi-objective optimization: classical and evolutionary approaches. Recent advances in evolutionary multi-objective optimization, 1-30.
[5]. Gunantara, N. (2018). A review of multi-objective optimization: Methods and its applications. Cogent Engineering, 5(1), 1502242.

Abstract : In this research paper, we present a comprehensive study on the solution of (2xn) and (nx2) fuzzy game problems using the graphical method, where the payoffs are represented by Hexagonal Fuzzy Numbers (HFNs). Hexagonal fuzzy numbers are particularly effective in modeling uncertainty due to their flexibility and ability to represent a wider range of imprecision compared to triangular and trapezoidal fuzzy numbers.
The primary objective of this study is to develop a systematic and efficient approach for solving two-person zero-sum fuzzy.....

Keywords: Fuzzy Numbers; Hexagonal Fuzzy Number; Fuzzy Game Problem; Graphical Method

[1]. Sharma A, Badal D. Solution Of Fuzzy Matrix Game Problem By Method Of Oddments Using Octadecagonal Fuzzy Numbers. International Journal Of Trends In Emerging Research And Development. 2025;3(5):80-85.
[2]. Barya V, Sharma A, Badal D. A New Octadecagonal Fuzzy Number And Its Fuzzy Arithmetic Operations. Vidyawarta. 2023;45:47-49.
[3]. Sharma A, Badal D. Solution Of Fuzzy Game Problem Using Tridecagonal Fuzzy Number. Review Of Business And Technology Research. 2019;16:129-134.
[4]. Arockiaraj J.J, Sivasankari N. Solution Of Fuzzy Game Problem Using Hexagonal Fuzzy Numbers. International Journal Of Mathematics And Its Application. 2016; 4:381-387.
[5]. Nayak, P.K, Pal M. Solution Of Rectangular Interval Games Using Graphical Method. Tamsui Oxford Journal Of Mathematical Sciences. 2016; 22(1):95-115.

Abstract : A partition, P, of the natural number, n, is perfect if (a) every natural number less than n has a partition of terms of P, and (b) these partitions are unique. Thus, 2 + 2 + 1 is a perfect partition of 5, while 3 + 2 + 1 is not a perfect partition of 6.....

[1]. M.Lewinter, Et Al, The Saga Of Mathematics: A Brief History. Prentice-Hall, 2002.
[2]. M.Lewinter, J.Meyer, Elementary Number Theory With Programming, Wiley, 2015.
[3]. Goulden And Jackson, Combinatorial Enumeration, Wiley, 1983.

Abstract : This study presents a unique method based on Rough Sets Theory (RST) and Differential Evolution (DE) to improve multi-objective optimization. In many different domains where it is necessary to simultaneously optimize conflicting objectives, multi-objective optimization problems are common. The research provides a novel approach to multi-objective optimization dubbed DEMORS (Differential Evolution for Mult objective Optimization with Random Sets), which consists of two phases: a local search phase using rough sets following the first Differential......

Keywords: Multi-Objective, Optimization, Differential, Evolution, Rough Set, Theory

[1]. Abdolrazzagh-Nezhad, M., Radgohar, H., & Salimian, S. N. (2020). Enhanced cultural algorithm to solve multi-objective attribute reduction based on rough set theory. Mathematics and Computers in Simulation, 170, 332-350.
[2]. Hancer, E. (2019). Fuzzy kernel feature selection with multi-objective differential evolution algorithm. Connection Science, 31(4), 323-341.
[3]. Hancer, E. (2020). A new multi-objective differential evolution approach for simultaneous clustering and feature selection. Engineering applications of artificial intelligence, 87, 103307.
[4]. Hancer, E. (2020). New filter approaches for feature selection using differential evolution and fuzzy rough set theory. Neural Computing and Applications, 32(7), 2929-2944.
[5]. Meenachi, L., & Ramakrishnan, S. (2020). Differential evolution and ACO based global optimal feature selection with fuzzy rough set for cancer data classification. Soft computing, 24(24), 18463-18475

Abstract : In the present work, an advanced Feed Forward Neural Network (FFNN) and Bayesian regularization algorithm-based method is implemented to solve 2nd order stiff ordinary differential equations and system of ordinary differential equations. Using proposed method, the problems are solved for various time steps and comparisons are made with available analytical solutions and other existing methods. Various test problems have been simulated using proposed FFNN model and accuracy has been acquired with less calculation efforts and time. The outcome of the work is showing good hope to use artificial neural network methods to solve various types of higher order linear and nonlinear stiff differential equations in near future.

Keywords: Feed Forward Neural Network, Multilayer Perceptron Neural Network, Stiff Ordinary Differential Equations, Back Propagation Algorithm, Bayesian Regularization Algorithm.

[1]. Suyong K., Weiqi J., Sili D., Yingbo M., Christopher R., “Stiff Neural Ordinary Differential Equations,” Chaos: An Interdisciplinary Journal Of Nonlinear Science, Vol. 31, No. 9, Pp. 093122, 2021. DOI: 10.1063/5.0060697
[2]. Weiqi J., Weilun Q., Zhiyu S., Shaowu P., Sili D., “Stiff-PINN: Physics-Informed Neural Network For Stiff Chemical Kinetics,” Journal Of Physical Chemistry, Vol. 125, No. 36, Pp. 8098-8106, 2021. DOI: 10.1021/Acs.Jpca.1c05102
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[5]. Malhotra S., “Simulation Of Navier Stokes Equation Represents The Transient Model Of Single Tube Heat Exchanger With Vapor-Liquid Two Phase Flow Inside”, International Journal Of Emerging Technologies In Computational And Applied Sciences, Vol. 5, No. 5, Pp. 540-547, 2013. Http://Citeseerx.Ist.Psu.Edu/Viewdoc/Download?Doi=10.1.1.380.7741&Rep=Rep1&Type=Pdf