International Journal of Applied Nonlinear Science (4 papers in press)

Regular Issues

• Fuzzy inventory modelling: addressing uncertainty in economic order quantity analysis within nonlinear science  
  by Soumendra Kumar Patra, Pragyan Parimita Sarangi, Nirmal Kumar Routra, Alok Kumar Jagadev, Bijay Kumar Paikaray 
  Abstract: Generally, in deriving the solution of economic order quantity (EOQ) inventory model, we consider deterioration rate, holding cost and ordering cost as constant. But in the case of real life problems, the above case is not actually constant but slightly disturbed from their original crisp value. In this paper, fuzzy inventory model is developed considering deterioration rate, holding cost and ordering cost as fuzzy variables. Because in practice, it is not always easy to determine the rate of deterioration precisely. In most of the cases it is uncertain in nature; therefore, it becomes reasonable to consider the vagueness and uncertainty of deterioration rate in fuzzy environment. These variables are represented by trapezoidal membership function. The function principle is applied to obtain an optimum total fuzzy cost along with optimum order quantity and optimum shortage quantity.
  Keywords: fuzzy membership function; fuzzy deterioration rate; trapezoidal numbers; function principle; defuzzification.
  DOI: 10.1504/IJANS.2024.10066705
   
  
• Solving fuzzy fractional differential equations using fuzzy fractional fourth order Runge-Kutta method based on centroidal mean  
  by S. Luvis Savla, R. Gethsi Sharmila 
  Abstract: In applied sciences and engineering, fuzzy fractional differential equations (FFDEs) are a crucial topic. The objective of this research is to determine the approximate solution to FFDEs. In the Caputo notion, the fractional derivatives are regarded. The fuzzy fractional fourth order Runge-Kutta method based on centroidal mean (FFRK4CeM) is developed to solve fuzzy fractional initial value problems (FFIVPs). The fuzzy fractional fourth-order Runge-Kutta method and the fuzzy fractional fourth-order Runge-Kutta method based on centroidal mean can be compared. Graphical results show the symmetry between the solutions lower and upper -cut representations and satisfy the convex symmetric triangular fuzzy number. As the step size decreases, the approximate solution increasingly aligns with the result. Finally, the results indicate that the proposed method is user-friendly, precise, straightforward, and efficient for addressing both linear and nonlinear first-order finite initial value problems (FFIVP).
  Keywords: Mittag-Leffler function; Caputo fractional derivative; fuzzy fractional differential equations; FFDEs; fuzzy fractional initial value problem; FFIVP; fuzzy fractional fourth order Runge-Kutta; FFRK4; fuzzy fractional fourth order Runge-Kutta method based on centroidal mean; FFRK4CeM; triangular fuzzy number; Zadeh’s extension principle; linear triangular FFIVP; nonlinear triangular FFIVP.
  DOI: 10.1504/IJANS.2024.10067543
   
  
• A machine learning-based nonlinear system for optimising crop selection and ensuring agricultural prosperity  
  by Mamata Garanayak, Swagatika Tripathy, Subrat Kumar Parida, Monalisa Panda, Bijay Kumar Paikaray, Fatimun Nisha 
  Abstract: Our nations economic prosperity is significantly influenced by agriculture. Investments in agricultural research and extension have consistently demonstrated excellent rates of return in Asia and the Pacific. In light of the impending difficulties in politics, the environment, and the economy, the cultivation of particular crops at specific times remains the primary issue that needs to be resolved. In this work, we create a prototype for recommendations that will first speculate on the kind of crop a farmer can cultivate based on the type of soil and environmental conditions (temperature, moisture, and rainstorm). The recommendation system will suggest five additional crops that are comparable to the predicted crop following the prediction. Prototypes relied on machine learning that have been shown to be effectual for predicting the best harvest can be utilised to accomplish this. The crops are predicted and recommended by the prototype with an accuracy of about 99.09%.
  Keywords: cultivation; crop counsel prototype; precision agriculture; recommendation prototype.
  DOI: 10.1504/IJANS.2024.10067544
   
  
• Interpreted social graph traversal algorithms for enhanced recommendation precision and efficiency in computational applications  
  by Jayanta Mondal, Sandipan Pine, Bijay Kumar Paikaray, Amita Yadav, Shalu Mehta 
  Abstract: The social network recommendation algorithms focusing on two novel approaches: interpreted social breadth first search (BFS) and the interpreted social depth first search (DFS). These algorithms aim to enhance the recommendation process by leveraging insights from social network analysis, graph traversal techniques, and interpreted relevance measures. This investigation utilising a real dataset sourced from Amazon, we compare the performance of BFS and DFS against conventional recommendation methods such as item-based collaborative filtering and hybrid approaches. This research finding reveals that BFS and DFS not only exhibit commendable precision but also demonstrate superior efficiency in terms of runtime. Moreover, our analysis indicates that these algorithms effectively narrow down the search space within the dataset, contributing to computational savings. This report sheds light on the potential of integrating social network structures and Interpreted user profiles into recommendation systems, offering valuable insights for researchers and practitioners in the field of recommender systems.
  Keywords: social network analysis; recommender systems; graph searching algorithms; user-based preferences; computational applications.
  DOI: 10.1504/IJANS.2024.10067729