Intelligent solution predictive networks for non-linear tumor-immune delayed model

Anwar, Nabeela; Ahmad, Iftikhar; Kiani, Adiqa Kausar; Shoaib, Mohammed; Raja, Muhammad Asif Zahoor

doi:10.1080/10255842.2023.2227751

Cited by 15 publications

(3 citation statements)

References 73 publications

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“…A two-step process called the ADS numerical solver is used to solve an ODE [63,64]. Initially, the predictive stage provides a rough approximation of the target outcome in order to utilize an explicit technique.…”

Section: Adams Methodsmentioning

confidence: 99%

Numerical treatment for mathematical model of farming awareness in crop pest management

Anwar,

Ahmad,

Kiani

et al. 2023

Front. Appl. Math. Stat.

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The most important factor for increasing crop production is pest and pathogen resistance, which has a major impact on global food security. Pest management also emphasizes the need for farming awareness. A high crop yield is ultimately achieved by protecting crops from pests and raising public awareness of the devastation caused by pests. In this research, we aim to investigate the intricate impacts of nonlinear delayed systems for managing crop pest management (CPM) supervised by Ordinary Differential Equations (ODEs). Our focus will be on highlighting the intricate and often unpredictable relationships that occur over time among crops, pests, strategies for rehabilitation, and environmental factors. The nonlinear delayed CPM model incorporated the four compartments: crop biomass density [B(t)], susceptible pest density [S(t)], infected pest density [I(t)], and population awareness level [A(t)]. The approximate solutions for the four compartments B(t), S(t), I(t), and A(t) are determined by the implementation of sundry scenarios generated with the variation in crop biomass growth rate, rate of pest attacks, pest natural death rate, disease associated death rate and memory loss of aware people, by means of exploiting the strength of the Adams (ADS) and explicit Runge-Kutta (ERK) numerical solvers. Comparative analysis of the designed approach is carried out for the dynamic impacts of the nonlinear delayed CPM model in terms of numerical outcomes and simulations based on sundry scenarios.

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Section: Adams Methodsmentioning

confidence: 99%

Numerical treatment for mathematical model of farming awareness in crop pest management

Anwar,

Ahmad,

Kiani

et al. 2023

Front. Appl. Math. Stat.

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“…In recent years, the search of numerical and exact solutions for nonlinear equations has gained particular prominence as a popular and captivating domain both in mathematics and physics. Various numerical methods contribute to the solution, such as Runge–Kutta method 11 – 13 , the Bayesian regularization technique (BRT) 14 – 17 , Levenberg–Marquardt approach (LMA) 18 , 19 , the shooting method 20 – 22 , the bvp4c technique 23 – 25 , the Keller box method 26 – 28 , the Lobatto IIIA method 29 , etc. Also researchers have developed several effective, powerful and efficient exact methods for uncovering the solutions of these equations.…”

Section: Introductionmentioning

confidence: 99%

Exact soliton solutions and the significance of time-dependent coefficients in the Boussinesq equation: theory and application in mathematical physics

Kawser,

Akbar,

Khan

et al. 2024

Sci Rep

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This article effectively establishes the exact soliton solutions for the Boussinesq model, characterized by time-dependent coefficients, employing the advanced modified simple equation, generalized Kudryashov and modified sine–Gordon expansion methods. The adaptive applicability of the Boussinesq system to coastal dynamics, fluid behavior, and wave propagation enriches interdisciplinary research across hydrodynamics and oceanography. The solutions of the system obtained through these significant techniques make a path to understanding nonlinear phenomena in various fields, surpassing traditional barriers and further motivating research and application. Significant impacts of the coefficients of the equation, wave velocity, and related parameters are evident in the profiles of soliton-shaped waves in both 3D and 2D configurations when all these factors are treated as variables, which are not seen in the case for constant coefficients. This study enhances the understanding of the significant role played by nonlinear evolution equations with time-dependent coefficients through careful dynamic explanations and detailed analyses. This revelation opens up an interesting and challenging field of study, with promising insights that resonate across diverse scientific disciplines.

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“…The current investigations present the solutions of learning language differential model by using the artificial intelligence (AI) procedure of scale conjugate gradient neural network (SCJGNN). In recent years, the stochastic computing solvers have been exploited in various applications, like susceptible, exposed, infected and recovered nonlinear model based on the worms propagation using the networks of wireless sensor 28 , eye surgery differential system 29 , 30 , predictive networks for the delayed model of tumor-immune 31 , HIV infection system 32 , 33 , delay differential model based avian influenza 34 , Lane-Emden model 35 , 36 , influenza-A epidemic system 37 , smoking model 38 , 39 , multi-delayed model based tumor oncolytic virotherapy 40 , thermal explosion model 41 , and biological differential system 42 . Some novel features of this research are presented as: The solutions of the learning language differential model using the stochastic AI along with the SCJGNN solver are presented successfully.…”

Section: Introductionmentioning

confidence: 99%

An artificial neural network approach for the language learning model

Sabir,

Ben Said,

Al-Mdallal

2023

Sci Rep

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The current study provides the numerical solutions of the language-based model through the artificial intelligence (AI) procedure based on the scale conjugate gradient neural network (SCJGNN). The mathematical learning language differential model is characterized into three classes, named as unknown, familiar, and mastered. A dataset is generalized by using the performance of the Adam scheme, which is used to reduce to mean square error. The AI based SCJGNN procedure works by taking the data with the ratio of testing (12%), validation (13%), and training (75%). An activation log-sigmoid function, twelve numbers of neurons, SCJG optimization, hidden and output layers are presented in this stochastic computing work for solving the learning language model. The correctness of AI based SCJGNN is noted through the overlapping of the results along with the small calculated absolute error that are around 10–06 to 10–08 for each class of the model. Moreover, the regression performances for each case of the model is performed as one that shows the perfect model. Additionally, the dependability of AI based SCJGNN is approved using the histogram, and function fitness.

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Intelligent solution predictive networks for non-linear tumor-immune delayed model

Cited by 15 publications

References 73 publications

Numerical treatment for mathematical model of farming awareness in crop pest management

Numerical treatment for mathematical model of farming awareness in crop pest management

Exact soliton solutions and the significance of time-dependent coefficients in the Boussinesq equation: theory and application in mathematical physics

An artificial neural network approach for the language learning model

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