Optimization Algorithm Research Articles

EEG Based Emotion Analysis using Reinforced Spatio-Temporal Attentive Graph Neural and Context Net Techniques

EEG-based emotion Classification is used to distinguish and observe the mental state or emotions. Emotion classification using EEG is used for medical, security, and various other purposes. There are several deep learning and machine learning techniques that are used to classify EEG emotion signals. They do not provide sufficient accuracy and have higher complexity and a high error rate. In this manuscript, a novel Reinforced Spatio-Temporal Attentive Graph Neural Networks (RSTAGNN)and ContextNet for emotion classification with EEG signals is proposed (RSTAGNN-ContextNet-GWOA-EEG-EA). Here the input EEG signals are taken from two benchmark datasets namely as DEAP and K-EmoCon datasets. Then the input EEG signals are pre-processed and the features are extracted using ContextNet with Global principal component Analysis (GPCA). After that, the EEG signal emotions are classified using Reinforced Spatio-Temporal Attentive Graph Neural Networks (RSTAGNN) method. Then the weight parameters of the RSTAGNN are optimized using Glowworm Swarm Optimization Algorithm (GWOA). The proposed model classifies the EEG signal emotions with high accuracy. The performance of the proposed method using DEAP dataset attains higher accuracy by 24.05%, 12.64%relatedto existing systems, like DWT-SVM-EEG-EA-DEAP and GCNN-LSTM-EEG-EA-DEAP respectively and the performance of the proposed system using K-EmoCon dataset attains higher accuracy 32.64%, 15.65% related to existing systems, like SigRep-EEG-EA-K- EEG Based Emotion Analysis using Reinforced Spatio-Temporal Attentive Graph Neural and Context Net Techniques on Deap and K- EmoCon Article and CAT-EEG-EA-K-EmoCon respectively.

Multilevel thresholding based image segmentation using Masi entropy and moth-flame optimization algorithm

Multilevel thresholding based image segmentation using Masi entropy and moth-flame optimization algorithm

An energy‐efficient Chebyshev fire hawks optimization algorithm for energy balancing in sensor‐enabled Internet of Things

SummarySensor‐enabled systems have been used successfully in agricultural, healthcare, commercial, and military application domains. Recently, there has been significant interest in the intelligent applications of sensor‐enabled technologies, particularly in the domains of smart grid, Internet of Vehicles (IoV), body area networks, and the Internet of Things (IoT). In recent research, various protocols and algorithm are developed for effective energy‐efficient routing and energy balancing. These existing models have some issues like high energy consumption and minimum network life time. In order to overcome these existing issues, a novel cluster head selection and routing mechanism in a wireless sensor network (WSN) environment is proposed. The clustering process has been formed by an enhanced Taylor kernel fuzzy C‐means algorithm (TKFC‐means). The cluster head in the group of sensor nodes has been identified based on energy and distance calculation. Finally, the routing has been performed by a novel energy‐efficient Chebyshev fire hawks optimization‐based routing protocol to route data to the edge server, which helps to balance the energy effectively. This protocol takes into account various factors, including distance, cost, residual energy, load, temperature, latency, and overall energy. The proposed model can obtain a throughput value of 82 Mbps for the sensor nodes at 500 and an end‐to‐end delay of 3.6 at 500 sensor nodes. The packet delivery ratio and loss ratio attain 96.4% and 2.7%, respectively, with 500 sensor nodes in the proposed approach. The proposed method consumes 0.45 mJ of energy with 500 nodes. From this analysis, the proposed model can obtain better results than the existing compared models.

A crack identification scheme based on neural network surrogate model and XFEM

Abstract Crack detection and identification is of great significance to the safety issues of engineering structures. In this paper, an intelligent crack identification scheme based on extended finite element and neural network surrogate model is proposed to realize the accurate identification of crack parameters. The method firstly employs extended finite element forward analysis to obtain the displacement data of measurement points on geometric models with different crack lengths, and inputs them as sample data to train the agent model, establishes a neural network-based inverse analysis model for crack identification, and automatically updates the threshold and weight of the neural network by using the Gray Wolf optimization algorithm to finally compute the globally optimal results. In the screening of the surrogate model, this paper verifies the advantages of the neural network surrogate model in data fitting and crack information extraction by comparing and analyzing the characteristics of neural network, support vector machine and other surrogate models, and optimizing the neural network surrogate model by adopting the Gray Wolf optimization algorithm. Finally, several numerical examples of different types of cracks are given to verify the validity of the proposed method, and the results show that the proposed method can accurately invert the geometric information of cracks.

An Agent-Based Method for Feature Recognition and Path Optimization of Computer Numerical Control Machining Trajectories

In recent years, artificial intelligence technology has seen increasingly widespread application in the field of intelligent manufacturing, particularly with deep learning offering novel methods for recognizing geometric shapes with specific features. In traditional CNC machining, computer-aided manufacturing (CAM) typically generates G-code for specific machine tools based on existing models. However, the tool paths for most CNC machines consist of a series of collinear motion commands (G01), which often result in discontinuities in the curvature of adjacent tool paths, leading to machining defects. To address these issues, this paper proposes a method for CNC system machining trajectory feature recognition and path optimization based on intelligent agents. This method employs intelligent agents to construct models and analyze the key geometric information in the G-code generated during CNC machining, and it uses the MCRL deep learning model incorporating linear attention mechanisms and multiple neural networks for recognition and classification. Path optimization is then carried out using mean filtering, Bézier curve fitting, and an improved novel adaptive coati optimization algorithm (NACOA) according to the degree of unsmoothness of the path. The effectiveness of the proposed method is validated through the optimization of process files for gear models, pentagram bosses, and maple leaf models. The research results indicate that the CNC system machining trajectory feature recognition and path optimization method based on intelligent agents can significantly enhance the smoothness of CNC machining paths and reduce machining defects, offering substantial application value.

Research on aquatic biological signal recognition based on mode decomposition and deep learning

Abstract As an important research content in the field of marine biology and ecology, aquatic biological signal (ABS) recognition is of great significance for understanding marine ecosystems, protecting biodiversity, and monitoring environmental changes. To improve the recognition accuracy of ABS, this paper proposes a new ABS recognition method based on mode decomposition and deep learning. First, real ABS data were obtained from the public website, and some data were selected for the simulation experiment. Secondly, the signal is decomposed using improved variational mode decomposition by human evolutionary optimization algorithm (HEOA-VMD), and the decomposed intrinsic mode function (IMF) set is classified into low complexity and high complexity IMF using improved Lempel-Ziv complexity (ILZC) and reverse permutation entropy (RPE). Then, mutual information (MI) is used to select double eigenvectors from low and high complexity IMF, respectively, and recognition is performed based on the double eigenvectors using weighted-convolutional neural network (CNN)-bidirectional gate recurrent unit (BIGRU)-Attention model. Finally, the proposed ABS recognition method is applied to both chaotic and real signals, and additional proof of universality is performed using real signals. The result of the study shows that the accuracy of the proposed method for the recognition of chaotic and real signals is as high as 97.3% and 98.0%. In conclusion, the research on ABS recognition in this paper is successful and has a broad application prospect.

A Novel Pelican Bird Optimization Algorithm based Optimal Allocation of Combined DGs and EVCSs for Improving Voltage Stability of RDS

Objectives: The main objective of the proposed investigation is to enhance the voltage stability of Radial Distribution System (RDS). It is achieved by proper allocation between combined Electric Vehicle Charging Stations (EVCSs) and Distributed Generators (DGs) in RDS. Here, three objective functions such as Real Power loss, Average Voltage Devotion Index (AVDI) and Voltage Stability Index (VSI) are considered. Methods: The multi-objective optimization problem is implemented using a novel and an effective Pelican Optimization Algorithm (POA) to attain the best compromised solutions. POA is a nature inspired parameter less optimization tool and it is well suited for solution of complex, non-linear voltage stability problem. It determines the most excellent site and size of the EVCSs and DG units. The control parameter of POA considered are population size as 40, the number of variables as 8 and the maximum number of iterations as 100. MATLAB 14.0 platform is utilized for the projected problem. Findings: The bench mark test system of IEEE-33 node system is considered to authenticate the presentation of planned POA methodology. The outcomes such as minimized power loss and AVDI, improved voltage at each bus and VSI are presented. The percentage of loss reduction 39.30% is enhanced, when compared with the base case approach and 5.42 % more improved than the other existing methods like TLBO and HHO approaches. Similarly, the VSI 24.96 % is improved, when compared with the base case method and 1.58 % better than the other obtainable methods. Novelty: The POA is a newly urbanized and well-designed optimization algorithm. It efficiently reaches the global optimal solutions with less time duration. The comparative study with the existing algorithm of TLBO and HHO is considered to prove the performance of Projected POA approach. Keywords: Radial distribution system, Distributed Generators, Electric Vehicles, Charging Stations, Voltage stability, Network loss reduction, Pelican Optimization Algorithm

UTILIZING DEEP LEARNING AND PERSUASIVE TECHNOLOGY TO INCREASE USER TRUST IN DIGITAL PAYMENTS

The paper examines the foundations of user trust in digital payments, using quantitative analysis of data from 277 participants. The study focuses on the associations between perceived security, convenience, privacy protection, and user trust in digital payments. Our findings suggest that all three attributes, safety, security, and privacy, are powerful positive correlates of trust. Furthermore, past experience with digital payments (PEDP) modifies these relationships, suggesting that the impact of these attributes on trust can have significant implications for engaged user digital payments are greatly enhanced We can use persuasive design principles and advanced deep learning to enhance user trust. The study proposes a hybrid deep learning model combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs) for enhanced feature extraction and temporal sequence analysis of user interactions and preferences within digital payment environments. This model is complemented by a hybrid optimization algorithm merging the Zebra Optimization Algorithm (ZOA) and Seagull Optimization Algorithm (SOA), aimed at refining system performance and user engagement through iterative enhancement of persuasive elements. This paper lays the groundwork for further research on other factors affecting the use of trust and cultural diversity in digital payment channels. Combining mind-blowing technologies with sophisticated deep learning techniques, it seeks to develop digital payment systems that not only secure transactions but also provide lasting user trust, satisfaction planning and implementation improvements.

DESIGN OF HYBRID OPTIMIZED PI CONTROLLER FOR POWER CONDITIONING CIRCUIT OF PIEZOELECTRIC ENERGY HARVESTER

Abstract Piezoelectric Energy Harvesters (PEH) are vital in the present day to encourage advancing technology through producing sustainable energy and dealing with economic and ecological issues associated with batteries. The required stable energy production from the PEH is challenging due to its erratic behaviour with input. As such, a power conditioning circuit with an optimized controller is proposed in this paper. The Proportional Integral (PI) controller is utilized to control the boost converter of the PEH system. The conventional approach of selecting the controller parameters, such as Proportional gain (KP) and Integral gain (Ki), fails to produce optimal outcomes and is time-consuming. Metaheuristic optimization techniques such as Aquila Optimization (AO), African Vulture’s Optimization Algorithm (AVOA) and Hybrid Aquila Optimizer-based African Vulture’s Optimization Algorithm (HAOAVOA) are used to optimize the controller parameter. The different stages of PEH system (Rectifier, boost converter without controller, boost converter with un optimized controller and boost converter with optimized controller) are simulated and analysed using MATLAB-SIMULINK® software. An experimental study is executed to validate the performance of the PEH system with optimized controller. The experimentally obtained response is compared with simulation results. In which the HAOAVOA optimized PI controller gives the better response. Furthermore, the power conditioning circuit with a hybrid optimized controller produces an output of 12 V for the input of 6 V.

Multi-Modal Machine Learning to Predict the Energy Discharge Levels from a Multi-Cell Mechanical Draft Cooling Tower

An artificial neural network was developed to augment the accuracy of a physically based computer model in relating heat discharge to visible plume volume of a 12-cell mechanical draft cooling tower. In a previous study, Savannah River National Laboratory developed a 1D model to capture the average power plant discharge levels via analysis of a series of visual images but was unable to accurately predict individual cases, resulting in an overall average error of about 5%, but individual comparisons resulted in an R2 of 0.36. Three optimization algorithms were applied to better fit the entrainment coefficients, and the artificial neural network model was applied to 289 cases of a 12-cell mechanical draft cooling tower power generation facility. Two artificial neural networks configurations consisted of 10 and 47 nodes that used as input readily available plant data, observed cooling tower plume conditions, observed operational conditions, local and regional weather, and the predicted plume volume from the physical model; the individual predictions’ accuracy improved to R2>0.95. This article concludes the sensitivities for the 1D model and additional actions to progress this field of study as well as applications for cooling tower monitoring. This strategy demonstrated an encouraging first step towards using multi-modal artificial neural network machine learning technology for information fusion to estimate power levels from external observations.

Artificial neural network infused quasi oppositional learning partial reinforcement algorithm for structural design optimization of vehicle suspension components

Artificial neural network infused quasi oppositional learning partial reinforcement algorithm for structural design optimization of vehicle suspension components

Research on mine water source classifications based on BO-CatBoost.

Mine water surge is one of the main safety risks in coal mines. This research offers a novel mine water source identification model (BO-CatBoost) to successfully avoid and control mine sudden water catastrophes by properly identifying the sources of mine water. First, the classification model is trained and built using the Categorical Boosting (CatBoost) algorithm. The Gaussian process Bayesian optimization (BO) algorithm is used to optimize parameters, and the optimal parameter combination is integrated into the CatBoost algorithm to build the BO-CatBoost mine water source identification model, which further improves the accuracy of mine water source identification. The model was also applied to the Pingdingshan mine to verify the practicality of the model. Then, 29 groups of unknown water sources in Pingdingshan were selected as validation samples for the model and compared with the conventional CatBoost, Light Gradient Boosting Machine (LightGBM), and Extreme Gradient Boosting (Xgboost) models. The comparison results demonstrate that the accuracy of LightGBM, Xgboost, CatBoost, and BO-CatBoost models can reach 69%, 79.3%, 79.3%, and 100% respectively, and the RMSE is 0.947, 0.643, 0.719, and 0.0 respectively. The comprehensive analysis shows that, when it comes to mine water source detection, the BO-CatBoost model performs noticeably better than other models in terms of discriminative accuracy and generalization capacity. Lastly, the multi-output prediction and decision-making process of the BO-CatBoost water source identification model is visualized by the interpretability analysis performed with the SHAP approach. The research demonstrates that the BO-CatBoost model can more precisely and impartially identify mine water sources, offering fresh concepts for mine water source detection.

Research on a Chassis Stability Control Method for High-Ground-Clearance Self-Propelled Electric Sprayers

In response to the complex working conditions and poor driving stability of high-clearance self-propelled sprayers, a nonlinear model of the chassis power system was established based on the independently controllable torque of each wheel of the developed electric sprayer. A layered-architecture chassis drive control strategy was formulated, and a stability control framework comprising an instability judgment module, an upper controller, and a lower controller was constructed based on the analysis of the impact of the centroid slip angle, the yaw rate, and the wheel slip rate on driving stability. An ideal reference model was established based on the seven-degree-of-freedom model of the sprayer, and the current state of the sprayer body was determined using the instability judgment module. A drive anti-slip controller and a yaw moment controller based on fuzzy PID theory and sliding mode control theory were designed. Additionally, an optimal torque distribution algorithm was developed based on tire characteristics to rationally allocate drive torque to each wheel, ensuring the stability of the sprayer during operation. Simulation tests were conducted using MATLAB/Simulink to evaluate the sprayer under four different driving conditions during transport and field operations. The test results showed that the “SMC + optimal distribution” control method in the chassis stability control strategy reduced the maximum deviations of the yaw rate and centroid slip angle by an average of 89.5% and 13.6%, respectively, compared to no control. The wheel slip rate during straight driving was well maintained at around 15%, enhancing the driving stability of the sprayer.

Constructing machine learning models based on non-contrast CT radiomics to predict hemorrhagic transformation after stoke: a two-center study

PurposeMachine learning (ML) models were constructed according to non-contrast computed tomography (NCCT) images as well as clinical and laboratory information to assess risk stratification for the occurrence of hemorrhagic transformation (HT) in acute ischemic stroke (AIS) patients.MethodsA retrospective cohort was constructed with 180 AIS patients who were diagnosed at two centers between January 2019 and October 2023 and were followed for HT outcomes. Patients were analyzed for clinical risk factors for developing HT, infarct texture features were extracted from NCCT images, and the radiomics score (Rad-score) was calculated. Then, five ML models were established and evaluated, and the optimal ML algorithm was used to construct the clinical, radiomics, and clinical-radiomics models. Receiver operating characteristic (ROC) curves were used to compare the performance of the three models in predicting HT.ResultsBased on the outcomes of the AIS patients, 104 developed HT, and the remaining 76 had no HT. The HT group consisted of 27 hemorrhagic infarction (HI) and 77 parenchymal-hemorrhage (PH). Patients with HT had a greater neutrophil-to-lymphocyte ratio (NLR), baseline National Institutes of Health Stroke Scale (NIHSS) score, infarct volume, and Rad-score and lower Alberta stroke program early CT score (ASPECTS) (all p < 0.01) than patients without HT. The best ML algorithm for building the model was logistic regression. In the training and validation cohorts, the AUC values for the clinical, radiomics, and clinical-radiomics models for predicting HT were 0.829 and 0.876, 0.813 and 0.898, and 0.876 and 0.957, respectively. In subgroup analyses with different treatment modalities, different infarct sizes, and different stroke time windows, the assessment accuracy of the clinical-radiomics model was not statistically meaningful (all p > 0.05), with an overall accuracy of 79.5%. Moreover, this model performed reliably in predicting the PH and HI subcategories, with accuracies of 82.9 and 92.9%, respectively.ConclusionML models based on clinical and NCCT radiomics characteristics can be used for early risk evaluation of HT development in AIS patients and show great potential for clinical precision in treatment and prognostic assessment.

Virtual Tools for Testing Autonomous Driving: A Survey and Benchmark of Simulators, Datasets, and Competitions

Traditional road testing of autonomous vehicles faces significant limitations, including long testing cycles, high costs, and substantial risks. Consequently, autonomous driving simulators and dataset-based testing methods have gained attention for their efficiency, low cost, and reduced risk. Simulators can efficiently test extreme scenarios and provide quick feedback, while datasets offer valuable real-world driving data for algorithm training and optimization. However, existing research often provides brief and limited overviews of simulators and datasets. Additionally, while the role of virtual autonomous driving competitions in advancing autonomous driving technology is recognized, comprehensive surveys on these competitions are scarce. This survey paper addresses these gaps by presenting an in-depth analysis of 22 mainstream autonomous driving simulators, focusing on their accessibility, physics engines, and rendering engines. It also compiles 35 open-source datasets, detailing key features in scenes and data-collecting sensors. Furthermore, the paper surveys 10 notable virtual competitions, highlighting essential information on the involved simulators, datasets, and tested scenarios involved. Additionally, this review analyzes the challenges in developing autonomous driving simulators, datasets, and virtual competitions. The aim is to provide researchers with a comprehensive perspective, aiding in the selection of suitable tools and resources to advance autonomous driving technology and its commercial implementation.

Simulation of Time-Sequence Operation Considering DC Utilization Hours of New Energy Base in Desert and Gobi Area

Direct current power transmission is a crucial method for consuming new energy in desert and Gobi regions. Given the issue of the inefficient use of transmission channels, this study develops a simulation model for the operational time sequence of new energy bases in these areas. The model integrates factors such as hours of DC power transmission, environmental impacts, economic considerations, and operational flexibility. Initially, the model addresses the significant variability and unpredictability of wind and solar energy in these regions by introducing an analysis method combining Gaussian Kernel Density Estimation (GKDE) with a Gaussian Mixture Model (GMM). Subsequently, a flexible DC power transmission operational model is formulated, taking into account different DC transmission modes. The model’s efficiency is enhanced by employing the Dung Beetle Optimization algorithm with nondominated sorting to minimize variable ranges and expedite solution times. The model’s effectiveness is demonstrated through a simulation applied to a new energy base in Gansu Province, Northwest China, confirming its validity.

Obtained using the improved Load Modelling Approach

Robust metrics to measure sustainability of such projects are still lacking. In this thesis, research is carried out to examine the sustainability of rural microgrids and then develop metrics to enhance how sustainability can be measured for these types of projects. The research further seeks to formulate and design an optimum, robust, sustainable and economical solar PV based microgrid solution for electrification of a remote area focusing on green areas that currently have no access to electricity. The objective is to achieve a system that is cost effective, reliable and sustainable. The results show that gender considerations have a significant impact on load profiles and a higher consumption is obtained when gender is considered. Next, an efficient and robust sizing approach for off-grid PV microgrid systems has been developed and named the ComµGrid Sizing Approach. This approach utilizes “Mixed Integer Linear Programming (MILP)” to optimally size the PV microgrid and the “ComµGrid Based Spatial Clustering of Applications with Noise (DBSCAN)” algorithm to aggregate load and meteorological data. MATLAB software is used to execute the optimization algorithm.

The Wheel–Rail Contact Force for a Heavy-Load Train Can Be Measured Using a Collaborative Calibration Algorithm

The wheel–rail contact force is a crucial indicator for ensuring the secure operation of a heavy-load train. However, obtaining the real-time wheel–rail contact force of a heavy-load train is a challenging task as, due to safety considerations, it is not possible to install instrumented wheelsets on heavy-load trains. This work presents a novel approach to quantify the wheel–rail contact force of a heavy-load train, utilizing a cooperative calibration methodology. First, a ground measurement platform for the wheel–rail contact force of a heavy-load train is constructed on a selected rail section. The railway inspection car’s wheel–rail contact force measurement system is fine-tuned using a multilayer perceptron calibration approach, and the ground platform then uses the results to fine-tune the railway inspection car’s examined wheelset. Second, based on actual measured data on the wheel–rail contact force of a heavy-load train, and using the golden jackal optimization algorithm, the multilayer perceptron correction approach is employed to create a data relationship mapping model. This model correlates the corrected data on the wheel–rail contact force obtained from the railway inspection car with the wheel–rail contact force of a heavy-haul train with an axle load of 25 tons, and the precision of the mapping is guaranteed. Finally, by combining the wheel–rail contact force correction method for the railway inspection car and the contact force mapping model between the railway inspection car and the heavy-load train, collaborative calibration of the wheel–rail contact force of the heavy-load train is realized. The experimental results under two working conditions show that this method can realize high-precision, real-time measurement of the wheel–rail contact force of a heavy-load train. For the working condition of a straight-line section, the calibration error was within 1.593 kN, and the MAPE was 0.105%; for the working condition of a curved-line section, the calibration error was 2.344 kN, and the RMSE was 184.72 N.

Motion Prediction and Object Detection for Image-Based Visual Servoing Systems Using Deep Learning

This study primarily investigates advanced object detection and time series prediction methods in image-based visual servoing systems, aiming to capture targets better and predict the motion trajectory of robotic arms in advance, thereby enhancing the system’s performance and reliability. The research first implements object detection on the VOC2007 dataset using the Detection Transformer (DETR) and achieves ideal detection scores. The particle swarm optimization algorithm and 3-5-3 polynomial interpolation methods were utilized for trajectory planning, creating a unique dataset through simulation. This dataset contains randomly generated trajectories within the workspace, fully simulating actual working conditions. Significantly, the Bidirectional Long Short-Term Memory (BILSTM) model was improved by substituting its traditional Multilayer Perceptron (MLP) components with Kolmogorov–Arnold Networks (KANs). KANs, inspired by the K-A theorem, improve the network representation ability by placing learnable activation functions on fixed node activation functions. By implementing KANs, the model enhances parameter efficiency and interpretability, thus addressing the typical challenges of MLPs, such as the high parameter count and lack of transparency. The experiments achieved favorable predictive results, indicating that the KAN not only reduces the complexity of the model but also improves learning efficiency and prediction accuracy in dynamic visual servoing environments. Finally, Gazebo software was used in ROS to model and simulate the robotic arm, verify the effectiveness of the algorithm, and achieve visual servoing.

Integrating machine learning for the optimization of polyacrylamide/alginate hydrogel

Abstract Hydrogels are highly promising due to their soft texture and excellent biocompatibility. However, the designation and optimization of hydrogels involve numerous experimental parameters, posing challenges in achieving rapid optimization through conventional experimental methods. In this study, we leverage machine learning algorithms to optimize a dual-network hydrogel based on a blend of acrylamide and alginate, targeting applications in flexible electronics. By treating the concentrations of components as experimental parameters and utilizing five material properties as evaluation criteria, we conduct a comprehensive property assessment of the material using a linear weighting method. Subsequently, we design a series of experimental plans using the Bayesian optimization algorithm and validate them experimentally. Through iterative refinement, we optimize the experimental parameters, resulting in a hydrogel with superior overall properties, including heightened strain sensitivity and flexibility. Leveraging the available experimental data, we employ a classification algorithm to separate the cut-off data. The feature importance identified by the classification model highlights the pronounced impact of acrylamide, ammonium persulfate, and N, N-methylene on the classification outcomes. Additionally, we develop a regression model and demonstrate its utility in predicting and analyzing the relationship between experimental parameters and hydrogel properties through experimental validation.