Specific Model Parameters Research Articles

A large scale multi institutional study for radiomics driven machine learning for meningioma grading

This study aims to develop and evaluate radiomics-based machine learning (ML) models for predicting meningioma grades using multiparametric magnetic resonance imaging (MRI). The study utilized the BraTS-MEN dataset’s training split, including 698 patients (524 with grade 1 and 174 with grade 2–3 meningiomas). We extracted 4872 radiomic features from T1, T1 with contrast, T2, and FLAIR MRI sequences using PyRadiomics. LASSO regression reduced features to 176. The data was split into training (60%), validation (20%), and test (20%) sets. Five ML algorithms (TabPFN, XGBoost, LightGBM, CatBoost, and Random Forest) were employed to build models differentiating low-grade (grade 1) from high-grade (grade 2–3) meningiomas. Hyperparameter tuning was performed using Optuna, optimizing model-specific parameters and feature selection. The CatBoost model demonstrated the best performance, achieving an area under the receiver operating characteristic curve (AUROC) of 0.838 [95% confidence interval (CI): 0.689–0.935], precision of 0.492 (95% CI: 0.371–0.623), recall of 0.838 (95% CI: 0.689–0.935), F1 score of 0.620 (95% CI: 0.495–0.722), accuracy of 0.729 (95% CI: 0.650–0.800), an area under the precision-recall curve (AUPRC) of 0.620 (95% CI: 0.433–0.753), and Brier score of 0.156 (95% CI: 0.122-0.200). Other models showed comparable performance, with mean AUROCs ranging from 0.752 to 0.784. The radiomics-based ML approach presented in this study showcases the potential for non-invasive and pre-operative grading of meningiomas using multiparametric MRI. Further validation on larger and independent datasets is necessary to establish the robustness and generalizability of these findings.

Viscoelastic modeling based force control framework for enhancing operation safety of soft tissue for orthopedic surgical robots

In robot-assisted laminectomy, precise control of touch force on the spinal cord during the dissection of ossified ligaments is critical to prevent dura mater tearing and ensure surgical safety. However, the complex viscoelastic characteristics of tissue at the lesion site pose a significant challenge for accurately applying touch force. This paper proposes a safe touch force servo framework based on tissue model identification and preoperative optimization of the force controller. First, a soft tissue model is established using elastic and viscoelastic elements, and the transfer function from tissue deformation to touch force is derived. Subsequently, the path and parameters of a safe pre-touch are designed, and force information required for tissue model identification is obtained through the pre-touch experiment. The model order and specific parameter values are then identified using a differential evolution algorithm. Next, force control simulation is conducted based on the identified model, and a specifically designed loss function is introduced to achieve preoperative tuning of the force controller. Finally, step force control experiments on sheep spines validate the effectiveness of the proposed method. This paper provides a safe and available touch motion control framework that can be expandable to impose precise force on various vulnerable soft tissues, with the potential for widespread application.

P10.24.B PREDICTION OF PCFDNA LEVELS, PROGRESSION-FREE SURVIVAL, AND OVERALL SURVIVAL THROUGH MACHINE-LEARNING MODELS BASED ON MRI-DERIVED RADIOMIC FEATURES IN PATIENTS WITH NEWLY DIAGNOSED GLIOBLASTOMA

Abstract BACKGROUND Circulating cell-free DNA (ccfDNA) is a promising tool for monitoring patients with high-grade gliomas (HGGs) in addition to follow-up with conventional (cMRI) and advanced MRI (aMRI), which harbors an enormous amount of quantitative subvisual data that can be used to build models predicting disease behavior. Examining the relationship between ccfDNA levels and quantitative features extracted from cMRI and aMRI gives further insight into this novel biomarker, aiming to combine the biological value of ccfDNA and the spatial resolution of image biomarkers to improve HGGs monitoring. MATERIAL AND METHODS Features were extracted from each T1 post-contrast and FLAIR images by using the FLAIR hyperintensity and enhancement automatic segmentation tool (Pyradiomics software, v2.2.0). The radiomic dataset underwent Z-score normalization, feature reduction, and feature selection. Highly correlated features were excluded using a threshold of 0.9 person-correlation-coefficient. The ‘F_regression’ function was used to select the most important features concerning the ccfDNA concentration, Overall survival (OS), and progression-free survival (PFS). This dataset was used as input for four machine learning models: Linear Regression (LR), Support Vector Regression (SVR), Random Forest (RF), and least absolute shrinkage and selection operator (LASSO). Hyperparameter tuning was performed at the first training iteration of each model using a 4-fold cross-validation GridSearchCV and model-specific parameters. The train-test strategy involved randomizing datasets into 80% training and 20% testing subsets across 100 iterations, ensuring robustness and reducing biases. RMSE values of each run were averaged based on sample test frequencies. Root means square error (RMSE) was used to assess the model’s performance. RESULTS 405 features were successfully extracted from T1 post-contrast and FLAIR images from both tumoral masks. A total of 287 were found to be highly correlated and, therefore, removed from the dataset. The ‘F_regression’ with a 90-percentile threshold separates 12 features divided into 3 shapes features relative to FLAIR segmentation, 6 features relative to the T1 post-contrast image, and 3 features for the FLAIR image. The SVR achieves the best RMSE performances when compared to other models when predicting the ccfDNA concentration, reaching an RMSE of 8.58. An analogous strategy was used for the models built to predict PFS and OS reaching an RMSE of 5.89 and 6.77, respectively. CONCLUSION Machine-learning models based on radiomic features combined with clinical variables are valuable tools for predicting OS and PFS in HGGs’ patients. The radiomic signature provides additional information compared to the tumoral volume to forecast ccfDNA levels. Combining both techniques could provide more precise assessment of disease status.

Analysis of Effects on Scientific Impact Indicators Based on Coevolution of Coauthorship and Citation Networks

This study investigates the coevolution of coauthorship and citation networks and their influence on scientific metrics such as the h-index and journal impact factors. Using a preferential attachment mechanism, we developed a model that integrated these networks and validated it with data from the American Physical Society (APS). While the correlations between reference counts, paper lifetime, and team sizes with scientific impact metrics are well-known, our findings demonstrate how these relationships vary depending on specific model parameters. For instance, increasing reference counts or reducing paper lifetime significantly boosts both journal impact factors and h-indexes, while expanding team sizes without adding new authors can artificially inflate h-indexes. These results highlight potential vulnerabilities in commonly used metrics and emphasize the value of modeling and simulation for improving bibliometric evaluations.

Latent disentanglement in mesh variational autoencoders improves the diagnosis of craniofacial syndromes and aids surgical planning

Background and objective:The use of deep learning to undertake shape analysis of the complexities of the human head holds great promise. However, there have traditionally been a number of barriers to accurate modelling, especially when operating on both a global and local level. Methods:In this work, we will discuss the application of the Swap Disentangled Variational Autoencoder (SD-VAE) with relevance to Crouzon, Apert and Muenke syndromes. The model is trained on a dataset of 3D meshes of healthy and syndromic patients which was increased in size with a novel data augmentation technique based on spectral interpolation. Thanks to its semantically meaningful and disentangled latent representation, SD-VAE is used to analyse and generate head shapes while considering the influence of different anatomical sub-units. Results:Although syndrome classification is performed on the entire mesh, it is also possible, for the first time, to analyse the influence of each region of the head on the syndromic phenotype. By manipulating specific parameters of the generative model, and producing procedure-specific new shapes, it is also possible to approximate the outcome of a range of craniofacial surgical procedures. Conclusion:This work opens new avenues to advance diagnosis, aids surgical planning and allows for the objective evaluation of surgical outcomes. Our code is available at github.com/simofoti/CraniofacialSD-VAE.

Evaluating the robustness of parameter estimates in cognitive models: A meta-analytic review of multinomial processing tree models across the multiverse of estimation methods.

Researchers have become increasingly aware that data-analysis decisions affect results. Here, we examine this issue systematically for multinomial processing tree (MPT) models, a popular class of cognitive models for categorical data. Specifically, we examine the robustness of MPT model parameter estimates that arise from two important decisions: the level of data aggregation (complete-pooling, no-pooling, or partial-pooling) and the statistical framework (frequentist or Bayesian). These decisions span a multiverse of estimation methods. We synthesized the data from 13,956 participants (164 published data sets) with a meta-analytic strategy and analyzed the magnitude of divergence between estimation methods for the parameters of nine popular MPT models in psychology (e.g., process-dissociation, source monitoring). We further examined moderators as potential sources of divergence. We found that the absolute divergence between estimation methods was small on average (<.04; with MPT parameters ranging between 0 and 1); in some cases, however, divergence amounted to nearly the maximum possible range (.97). Divergence was partly explained by few moderators (e.g., the specific MPT model parameter, uncertainty in parameter estimation), but not by other plausible candidate moderators (e.g., parameter trade-offs, parameter correlations) or their interactions. Partial-pooling methods showed the smallest divergence within and across levels of pooling and thus seem to be an appropriate default method. Using MPT models as an example, we show how transparency and robustness can be increased in the field of cognitive modeling. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

AutoRefl: active learning in neutron reflectometry for fast data acquisition

Neutron reflectometry (NR) is a powerful technique for interrogating the structure of thin films at interfaces. Because NR measurements are slow and instrument availability is limited, measurement efficiency is paramount. One approach to improving measurement efficiency is active learning (AL), in which the next measurement configurations are selected on the basis of information gained from the partial data collected so far. AutoRefl, a model-based AL algorithm for neutron reflectometry measurements, is presented in this manuscript. AutoRefl uses the existing measurements of a function to choose both the position and the duration of the next measurement. AutoRefl maximizes the information acquisition rate in specific model parameters of interest and uses the well defined signal-to-noise ratio in counting measurements to choose appropriate measurement times. Since continuous measurement is desirable for practical implementation, AutoRefl features forecasting, in which the optimal positions of multiple future measurements are predicted from existing measurements. The performance of AutoRefl is compared with that of well established best practice measurements for supported lipid bilayer samples using realistic digital twins of monochromatic and polychromatic reflectometers. AutoRefl is shown to improve NR measurement speeds in all cases significantly.

Consensus holistic virtual screening for drug discovery: a novel machine learning model approach

In drug discovery, virtual screening is crucial for identifying potential hit compounds. This study aims to present a novel pipeline that employs machine learning models that amalgamates various conventional screening methods. A diverse array of protein targets was selected, and their corresponding datasets were subjected to active/decoy distribution analysis prior to scoring using four distinct methods: QSAR, Pharmacophore, docking, and 2D shape similarity, which were ultimately integrated into a single consensus score. The fine-tuned machine learning models were ranked using the novel formula “w_new”, consensus scores were calculated, and an enrichment study was performed for each target. Distinctively, consensus scoring outperformed other methods in specific protein targets such as PPARG and DPP4, achieving AUC values of 0.90 and 0.84, respectively. Remarkably, this approach consistently prioritized compounds with higher experimental PIC50 values compared to all other screening methodologies. Moreover, the models demonstrated a range of moderate to high performance in terms of R2 values during external validation. In conclusion, this novel workflow consistently delivered superior results, emphasizing the significance of a holistic approach in drug discovery, where both quantitative metrics and active enrichment play pivotal roles in identifying the best virtual screening methodology.Scientific contributionWe presented a novel consensus scoring workflow in virtual screening, merging diverse methods for enhanced compound selection. We also introduced ‘w_new’, a groundbreaking metric that intricately refines machine learning model rankings by weighing various model-specific parameters, revolutionizing their efficacy in drug discovery in addition to other domains.Graphical

StructDiffusion: End-to-end intelligent shear wall structure layout generation and analysis using diffusion model

Shear wall structures are widely used in high-rise buildings. However, the design process of shear wall structures suffers from inefficiencies. This paper introduces StructDiffusion, an end-to-end intelligent system for shear wall layout generation and analysis. The proposed approach tackles the layout design task by employing a novel diffusion model architecture to generate conditional images. Key components of the proposed method include pretrained diffusion models, ControlNet for conditional control, and LoRA for efficient adaptation. Notably, the method allows for architectural plan images and basic textual design conditions as inputs, enabling manipulation of the generated layouts by adjusting attributes such as building height and seismic intensity. To evaluate the quality of the model's design swiftly, this paper presents a comprehensive evaluation framework that incorporates perceptual and structural validity metrics. Through experimental analyses, this paper demonstrates the effectiveness of the proposed model in generating layouts, surpassing the capabilities of GAN-based methods. Furthermore, our study investigates model-specific parameter configuration, text-guided capabilities, code compliance, and computational efficiency. StructDiffusion significantly enhances the automation and intelligence of structural engineering workflows. The framework effectively addresses challenges associated with instability, data scarcity, and limitations in assessment. This pioneering application of diffusion models opens up promising avenues for advancing data-driven structural design.

Investigating Model Dependencies for Obscured Active Galactic Nuclei: A Case Study of NGC 3982

Abstract X-ray spectroscopy of heavily obscured active galactic nuclei (AGN) offers a unique opportunity to study the circumnuclear environment of accreting supermassive black holes. However, individual models describing the obscurer have unique parameter spaces that give distinct parameter posterior distributions when fit to the same data. To assess the impact of model-specific parameter dependencies, we present a case study of the nearby heavily obscured low-luminosity AGN NGC 3982, which has a variety of column density estimations reported in the literature. We fit the same broadband XMM-Newton+NuSTAR spectra of the source with five unique obscuration models and generate posterior parameter distributions for each. By using global parameter exploration, we traverse the full prior-defined parameter space to accurately reproduce complex posterior shapes and inter-parameter degeneracies. The unique model posteriors for the line-of-sight column density are broadly consistent, predicting Compton-thick N H &gt; 1.5 × 1024 cm−2 at the 3σ confidence level. The posterior median intrinsic X-ray luminosity in the 2–10 keV band was found to differ substantially, however, with values in the range log L 2–10 keV/ erg s−1 = 40.9–42.1 for the individual models. We additionally show that the posterior distributions for each model occupy unique regions of their respective multidimensional parameter spaces and how such differences can propagate into the inferred properties of the central engine. We conclude by showcasing the improvement in parameter inference attainable with the High Energy X-ray Probe, with its uniquely broad, simultaneous, and high-sensitivity bandpass of 0.2–80 keV.

EXPLORING FRACTIONAL DYNAMICAL PROBES IN THE CONTEXT OF GENDER-STRUCTURED HIV–TB COINFECTION: A STUDY OF CONTROL STRATEGIES

The primary objective of this paper is to conduct an in-depth analysis of the intricacies inherent in a gender-structured model depicting the dynamics of HIV–TB coinfection. The model is articulated through a set of nonlinear fractional-order differential equations. Our focus is on elucidating the stability characteristics around equilibrium points, employing advanced fractional stability techniques. We leverage sensitivity analysis as a powerful tool to discern the specific model parameters exerting significant influence on the transmission and control of the coinfection, as gauged by the basic reproduction number. Through this, we aim to identify key factors that drive the spread or containment of the dual affliction. Furthermore, we delve into the realm of optimization by formulating a fractional optimal control problem. The objective is to design and implement two control strategies that effectively mitigate the prevalence of coinfection, demonstrating a state of perfect protection. Notably, our findings reveal that the implementation of control measures at a rate exceeding 80% suffices to eliminate the infection entirely. We anticipate that the insights garnered from this research will prove instrumental for policymakers in crafting strategic interventions to curtail the tragic impact of HIV–TB coinfection. Our scientifically grounded study serves as a foundation upon which comprehensive plans can be devised to stem the devastating consequences of this dual health challenge.

Characterizing Spatial Structure in Climate Model Ensembles

Abstract This paper presents a methodology that is designed for rapid exploratory analysis of the outputs from ensembles of climate models, especially when these outputs consist of maps. The approach formalizes and extends the technique of “intermodel empirical orthogonal function” analysis, combining multivariate analysis of variance techniques with singular value decompositions (SVDs) of structured components of the ensemble data matrix. The SVDs yield spatial patterns associated with these components, which we call ensemble principal patterns (EPPs). A unique hierarchical partitioning of variation is obtained for balanced ensembles in which all combinations of factors, such as GCM and RCM pairs in a regional ensemble, appear with equal frequency: suggestions are also proposed to handle unbalanced ensembles without imputing missing values or discarding runs. Applications include the selection of ensemble members to propagate uncertainty into subsequent analyses, and the diagnosis of modes of variation associated with specific model variants or parameter perturbations. The approach is illustrated using outputs from the EuroCORDEX regional ensemble over the United Kingdom.

Variational Bayes Ensemble Learning Neural Networks With Compressed Feature Space.

We consider the problem of nonparametric classification from a high-dimensional input vector (small n large p problem). To handle the high-dimensional feature space, we propose a random projection (RP) of the feature space followed by training of a neural network (NN) on the compressed feature space. Unlike regularization techniques (lasso, ridge, etc.), which train on the full data, NNs based on compressed feature space have significantly lower computation complexity and memory storage requirements. Nonetheless, a random compression-based method is often sensitive to the choice of compression. To address this issue, we adopt a Bayesian model averaging (BMA) approach and leverage the posterior model weights to determine: 1) uncertainty under each compression and 2) intrinsic dimensionality of the feature space (the effective dimension of feature space useful for prediction). The final prediction is improved by averaging models with projected dimensions close to the intrinsic dimensionality. Furthermore, we propose a variational approach to the afore-mentioned BMA to allow for simultaneous estimation of both model weights and model-specific parameters. Since the proposed variational solution is parallelizable across compressions, it preserves the computational gain of frequentist ensemble techniques while providing the full uncertainty quantification of a Bayesian approach. We establish the asymptotic consistency of the proposed algorithm under the suitable characterization of the RPs and the prior parameters. Finally, we provide extensive numerical examples for empirical validation of the proposed method.

Insight into the treatment strategy on pneumonia transmission with asymptotic carrier stage using fractional order modeling approach

Pneumonia remains a significant global health concern, claiming millions of lives annually. This study introduces a novel approach by developing and analyzing a Caputo fractional order pneumonia infection model that incorporates pneumonia asymptomatic carriers. Through a qualitative lens, we establish the existence and uniqueness of model solutions by applying the well-known Picard–Lindelöf criteria. Employing a next-generation approach, we compute the model's basic reproduction number, determine equilibrium points, and probe their stabilities. The main objective of this study is to investigate the transmission dynamics of pneumonia infection with a focus on asymptomatic carriers using fractional order modeling. Our findings reveal innovative outcomes as we showcase numerical simulations, providing a practical verification of the qualitative results. Notably, we explore the fractional order model solutions in-depth, examining the influence of specific model parameters and fractional orders on the dynamics of pneumonia disease transmission. The significant contributions of this study lie in advancing the theoretical foundation of infectious disease modeling, particularly in the context of pneumonia. Through rigorous analysis and numerical simulations, we provide valuable insights into the behavior of the proposed fractional order model. These findings hold practical implications for understanding and managing pneumonia transmission in real-world scenarios. Our study serves as a vital resource for researchers, policymakers, and healthcare practitioners involved in combating and preventing the spread of pneumonia, ultimately contributing to global efforts in public health.

Damage and failure of semi-rigid steel joints during progressive collapse

Ductile metal materials during a ductile failure will suffer from micro-void damage, simulation of which can contribute to the prediction of the macro fracture of steel structure. In this paper, the Gurson-Tvergaard-Needleman (GTN) constitutive model which describes the aggregate failure of metal voids is selected as the constitutive relation to investigating the damage evolution and fracture of semi-rigid joints in steel structures during a progressive collapse. During the process, specific parameters of the GTN model of steel are confirmed based on the combined results of the test and numerical simulation. The progressive collapse of two kinds of semi-rigid joints of steel pipe-to-H shaped steel were simulated by a nonlinear finite element method. A comparison between the simulation and test results verified that the GTN constitutive model can accurately simulate the fracture failure of the steel semi-rigid joints. Besides, a simplified joint failure model based on concentrated plastic hinge theory is proposed. Considering the damage and fracture of semi-rigid joints and the collision contact among different components, the progressive collapse of the semi-rigid steel frame structure is simulated by explicit dynamic analysis based on a finite particle method. Afterwards, the dynamic response of the structural progressive collapse can be predicted more accurately.

A Review on Local Failure Probability Sensitivity Analysis

When assessing the reliability of a system, a mathematical model is often defined to replicate the system’s behavior. The inputs of the system are then gathered into two categories, random inputs and deterministic inputs. The failure of the system depends on both categories and here we focus on the influence of the deterministic inputs. Local failure probability sensitivity analysis consists in computing the derivatives of the failure probability with respect to these deterministic parameters and is a fundamental step in reliability-based design optimization. These sensitivities also provide valuable insights into how specific model parameters affect the failure probability, allowing engineers and designers to make informed decisions about adjusting those parameters to enhance reliability. This article explores various techniques developed in the literature for assessing the sensitivity of failure probability with respect to distribution or design parameters. Depending on the nature of the deterministic parameters and the selected input space, different methods are available. The statistical characteristics of the resulting estimates as well as their computational cost are discussed here, for comparison purpose.

Parsing memory and nonmemory contributions to age-related declines in mnemonic discrimination performance: a hierarchical Bayesian diffusion decision modeling approach.

The mnemonic discrimination task (MDT) is a widely used cognitive assessment tool. Performance in this task is believed to indicate an age-related deficit in episodic memory stemming from a decreased ability to pattern-separate among similar experiences. However, cognitive processes other than memory ability might impact task performance. In this study, we investigated whether nonmnemonic decision-making processes contribute to the age-related deficit in the MDT. We applied a hierarchical Bayesian version of the Ratcliff diffusion model to the MDT performance of 26 younger and 31 cognitively normal older adults. It allowed us to decompose decision behavior in the MDT into different underlying cognitive processes, represented by specific model parameters. Model parameters were compared between groups, and differences were evaluated using the Bayes factor. Our results suggest that the age-related decline in MDT performance indicates a predominantly mnemonic deficit rather than differences in nonmnemonic decision-making processes. In addition, this mnemonic deficit might also involve a slowing in processes related to encoding and retrieval strategies, which are relevant for successful memory as well. These findings help to better understand what cognitive processes contribute to the age-related decline in MDT performance and may help to improve the diagnostic value of this popular task.

A mixed primary atomization model with quantification of aerodynamic, turbulence and cavitation effects

Spray atomization has a key impact on the combustion and emission performances of the engines. It involves a highly dynamic two-phase flow process consisting of two distinct stages: primary and secondary breakup. Due to their distinct physical characteristics, it is essential to model these processes separately using different numerical approaches within the spray breakup model. This study focuses on investigating the collective influence of aerodynamic force, cavitation, and turbulent fluctuations on primary breakup. To enhance the accuracy of spray simulations, a mixed primary atomization (MPA) model is proposed by integrating the Wu-Faeth theory along with the classical KH-RT model and the Sarre cavitation model. By comparing simulation results of both macroscopic and microscopic spray characteristics with experimental data under various conditions, the efficacy of the developed MPA model is validated. The sensitivity analyses of specific model parameters and the examination of the contribution ratios of different sub-models under various conditions enhance our understanding of the model calibration. Additionally, considering the complexity of near-nozzle sprays influenced by turbulent flows, cavitation, and interactions with the surrounding gas, a comparative analysis of the classical KH-RT model and the MPA model is conducted based on simulation results, specifically focusing on near-nozzle spray characteristics.

Functional clustering of neuronal signals with FMM mixture models

The identification of unlabeled neuronal electric signals is one of the most challenging open problems in neuroscience, widely known as Spike Sorting. Motivated to solve this problem, we propose a model-based approach within the mixture modeling framework for clustering oscillatory functional data called MixFMM. The core of the approach is the FMM (Frequency Modulated Möbius) waves, which are non-linear parametric time functions, flexible enough to describe different oscillatory patterns and simple enough to be estimated efficiently. In particular, specific model parameters describe the phase, amplitude and shape of the waveforms. A mixture model is defined using FMM waves as basic functions and gaussian errors, and an EM algorithm is proposed for estimating the parameters. Spike Sorting (SS) has received considerable attention in the literature, and different functional clustering approaches have been considered. We have conducted a fair comparative analysis of the MixFMM with three competitors. Two of them are traditional methods in functional clustering and widely used in Spike Sorting. The third is an approach that has proven superior to many others solving Spike Sorting problems. The datasets used for validation include benchmarking simulated and real cases. The internal and external validation indexes confirm a better performance of the MixFMM on real data sets against the three competitors and an outstanding performance in simulated data against traditional approaches.

A solar PV model parameters estimation based on an improved manta foraging algorithm with dynamic fitness distance balance

Accurately simulating and operating photovoltaic (PV) modules or solar cells requires determining specific model parameters based on experimental data. Extracting these parameters is crucial for analyzing system performance under various conditions such as temperature and sunlight variations. However, modeling solar photovoltaic systems is inherently nonlinear, which calls for an efficient algorithm. In this study, we employ the MRFO-dFDB (Manta Ray Foraging Optimization with dynamic Fitness Distance Balance) algorithm, which utilizes fitness distance balance to balance the exploration and exploitation of the search area when assessing parameters in solar PV models. By applying MRFO-dFDB to extract parameters from the STP6-120/36 and Photowatt-PWP201 solar modules, we observe exceptional predictive performance for both single diode (SDM) and double diode (DDM) models. MRFO-dFDB exhibits superior performance compared to state-of-the-art methods. It achieves lower Root-Mean-Square Error (RMSE) values, specifically &lt; 15.3 mA for the STP6-120/36 module and &lt;2.4 mA for the Photowatt-PWP201 module. Additionally, it demonstrates lower maximum errors of 39.02 mA and 5.33 mA, as well as lower power errors of 155.42 mW and 14.122 mW, for the STP6-120/36 and Photowatt-PWP201 solar modules, respectively. Furthermore, it exhibits excellent performance with faster computation speed (&lt; 30.1 seconds in all tests), further emphasizing its superiority.