Multilayer Perceptron Model Research Articles

Multi-Layer Perceptron and Radial Basis Function Networks in Predictive Modeling of Churn for Mobile Telecommunications Based on Usage Patterns

Customer retention is a key priority for mobile telecommunications companies, as acquiring new customers is significantly more costly than retaining existing ones. A major challenge in this field is predicting customer churn—users discontinuing services. Traditional predictive models such as rule-based systems often struggle with the complex, non-linear nature of customer behavior. To address this, we propose the use of deep learning techniques, specifically multi-layer perceptron (MLP) and radial basis function (RBF) networks, to improve the accuracy of churn predictions. However, while neural networks excel in predictive performance, they are often criticized for being “black-box” models, lacking interpretability. A real-world data set is considered, which originally contained information about 15,000 randomly selected clients. Various network structures and configurations are analyzed. The obtained results are compared with results generated using fuzzy rule-based and rough-set rule-based systems. The MLP model achieved an almost perfect accuracy of 0.999 with an F-measure of 0.989, outperforming traditional methods such as fuzzy rule-based and rough-set systems. Although the RBF model slightly lagged in accuracy, it demonstrated a superior recall of 0.993, indicating better identification of potential churners. These results demonstrate that neural network models significantly enhance predictive performance in churn modeling. The interpretability of the model is also discussed since it bears significance in real applications. Our contribution lies in showing that deep learning methods significantly enhance churn prediction accuracy, though the challenge of model interpretability remains a critical area for future work.

Exploring the inverse line-source scattering problem in dielectric cylinders with deep neural networks

Abstract This study presents a novel approach utilizing deep neural networks to
address the inverse line-source scattering problem in dielectric cylinders. By employing
Multi-layer Perceptron models, we intend to identify the number, positions, and
strengths of hidden internal sources. This is performed by using single-frequency
phased data, from limited measurements of real electric and real magnetic surface fields.
Training data are generated by solving corresponding direct problems, using an exact
solution representation. Through extended numerical experiments, we demonstrate
the efficiency of our approach, including scenarios involving noise, reduced sample
sizes, and fewer measurements. Additionally, we examine the empirical scaling laws
governing model performance and conduct a local analysis to explore how our neural
networks handle the inherent ill-posedness of the considered inverse problems.

Prediction of optical properties of uniaxial hyperbolic nanospheres via artificial neural network

In this study, absorption and scattering multilayer perceptron models are developed and validated to predict the optical spectra of uniaxial hyperbolic nanospheres. The finite difference time domain method is used to generate the dataset of the absorption and scattering optical spectra. The models’ optimal performance is achieved for 5 hidden layers and 80 neurons for the absorption and the same hidden layers with 120 neurons for the scattering. The predictions by the model on the test dataset give a low average mean squared error of 0.000145 for the absorption and 0.00071 for the scattering. We also performed a robustness test by using parameters outside the initial parameters used for the training and the predictions are in good agreement with the actual datasets for both absorption and scattering. This research shows the application of artificial neural networks to predict the optical properties of hyperbolic materials and lays the groundwork for developing more complicated neural network models to predict complex phenomena in hyperbolic metamaterials, which are faster and computationally less expensive than using conventional simulation methods. Hyperbolic metamaterials offer unique optical properties that can be used to design new optical devices.

Self-powered flexible wearable wireless sensing for outdoor work heatstroke prevention and health monitoring

Outdoor workers are susceptible to heatstroke in hot weather. Currently, there are no wearable devices on the market to assess the risk of heatstroke. This paper proposes a self-powered flexible wearable wireless sensing system for outdoor work heatstroke prevention and health monitoring (SWSM). SWSM consists of two parts, one part is a wearable heatstroke prevention patch based on flexible wireless sensing (WPFS) to monitor the wearer’s physiological indicators continuously. The other part is a self-powered device based on solar energy (SPDS), which can generate milliamp level current for powering the WPFS. With these indicators, the wearer’s activity category is confirmed by the multi-layer perceptron (MLP) model. WPFS contains a PVDF-HFD nanofibre cooling film (PNCF), flexible triboelectric nanogenerator (TENG)-based button (B-TENG), and functional sensing components. Temperatures under PNCF coverage can be up to 12% lower than environmental temperatures. B-TENG is used to adjust the mode. Based on the wearer’s physiological indicators a heatstroke risk assessment equation can be created to assess the potential risk of heatstroke. Finally, the data is displayed on the Internet of Things (IoT) platform. The system is wireless, self-powered, flexible wearable, and can be used for real-time prevention of heatstroke risk and health monitoring of outdoor workers.

Predicting and Analyzing Electric Bicycle Adoption to Enhance Urban Mobility in Belgrade Using ANN Models

The development of alternative environmentally friendly modes of transportation is becoming an increasingly promising solution in traffic-congested and polluted urban areas. E-bikes, as one of them, are recognized as an ecologically sustainable means of transportation that has significant potential to replace motorized modes of transportation that can improve urban mobility. Relying on artificial intelligence and considering an ecological approach when considering the acceptability of e-bikes by setting a direct question for users influences the development of an innovative way of understanding and evaluating the use of more sustainable modes of transportation. In this regard, this study aims to elucidate the main variables influencing the acceptability of e-bike use using artificial neural network (ANN) models—multilayer perceptron (MLP) and radial basis function (RBF). For training and testing the models, data from a random sample obtained through an online questionnaire, which was answered by 626 residents of Belgrade (Serbia), were used. A multilayer perceptron with nine and seven neurons in two hidden layers, respectively, hyperbolic tangent activation function in the hidden layer and identity function in the output layer, gave better results than the radial basis function model. With an accuracy of 89%, a precision of 83%, a recall of 79%, and an area under the receiver operating characteristic (ROC) curve of 0.927, the multilayer perceptron model recognized the influential variables in predicting acceptability. The results of the model indicate that the mileage traveled, the frequency of motorcycle use, the respondents’ awareness of the pollution in Belgrade, and the age of the respondents have the greatest influence on the acceptability of using e-bikes. In addition to majority acceptability (69.8%), the results obtained by the model can represent a useful basis for decision-makers when defining strategies for the development and application of e-bikes while reducing traffic congestion and environmental pollution in Belgrade.

Machine learning-based classification of varicocoele grading: A promising approach for diagnosis and treatment optimization.

Varicocoele is a correctable cause of male infertility. Although physical examination is still being used in diagnosis and grading, it gives conflicting results when compared to ultrasonography-based varicocoele grading. We aimed to develop a multi-class machine learning model for the grading of varicocoeles based on ultrasonographic measurements. Between January and May 2024, we enrolled unilateral varicocoele patients at an infertility clinic, assessing their varicocoele stages using the Dubin and Amelar system. We measured vascular diameter and reflux time at the testicular apex and the subinguinal region ultrasonography in both the supine and standing positions. Using these measurements, we developed four multi-class machine learning models, evaluating their performance metrics and determining which patient position and projection were most influential in varicocoele grading. We included 248 patients with unilateral varicocoele in the study, their average age was 26.61±4.95 years old. Of these, 212 had left-sided and 36 had right-sided varicocoeles. According to the Dubin and Amelar system, there were 66 grade I, 96 grade II, and 86 grade III varicocoeles. Among the models we created, the random forest (RF) model performed best, with an overall accuracy of 0.81±0.06, an F1 score of 0.79±0.02, a sensitivity of 0.69±0.02, and a specificity of 0.8±0.03. Vascular diameter measurement at the testicular apex in the supine position had the most impact on grading across all models. In support vector machine and multi-layer perceptron models, reflux time measurements from the subinguinal projection in the standing position contributed the most, while in RF and k-nearest neighbors models, measurements from the subinguinal projection in the supine position were the most influential. Machine learning methods have demonstrated superior accuracy in predicting disease compared to traditional statistical regressions and nomograms. These advancements hold promise for clinically automated prediction of varicocoele grades in patients. Tailored varicocoele grading for individuals has the potential to enhance treatment effectiveness and overall quality of life.

Machine learning based modeling for estimation of drug solubility in supercritical fluid by adjusting important parameters

Here, we employed machine learning models to predict how well Capecitabine drug would dissolve in supercritical carbon dioxide as the green solvent. The vision is to investigate the drug suitability for processing of nanodrugs with enhanced bioavailability in the body. In the employed data set, P (pressure) and T (temperature) serve as inputs, and Y, the solubility, is the only output for building the models. This study uses DT (Decision Tree) and MLP (Multilayer perceptron) as the core models. However, the raw and individual form of conventional algorithms may not provide accurate and general results. Ensemble methods like boosting improve the model performance. Also, single and ensemble models mounted on these models have hyper-parameters whose optimization affects the final models. Meta-heuristic algorithms are popular for tuning hyper-parameters. In this research, we used a new hybrid framework by coupling the basic models with the Adaboost algorithm (as an ensemble method) and PO and CS algorithms (as optimizers) to obtain four different models. The MLP model boosted with Adaboost and tuned with PO algorithm showed the best fitting accuracy among all models. This model reduces the RMSE error rate to 1.71, MSE to 2.92, and MAE to 1.42.

Intelligence analysis of membrane distillation via machine learning models for pharmaceutical separation

This study investigates simulation of pharmaceutical separation via membrane distillation process by computational simulation and machine learning modeling strategy. The efficacy of three regression models, i.e., Multi-layer Perceptron (MLP), Gamma Regression, and Support Vector Regression (SVR) in predicting the solute concentration, C(mol/m³), was evaluated. The hyper-parameters were optimized by fine-tuning the models using the Red Deer Algorithm (RDA). Computational analyses were carried out for removal of pharmaceuticals from solution by membrane distillation in continuous mode. Mass transfer and machine learning models were implemented focusing on concentration of solute in the feed section of membrane. Results indicate that the Multi-layer Perceptron model achieved great accuracy with an R2 of 0.9955, an MAE of 0.0084, and an RMSE of 0.0148, effectively capturing complex nonlinear relationships in the data. Gamma Regression also performed acceptably, with fitting R2 of 0.9214, showing its suitability for positively skewed data. The Support Vector Regression model, while capturing the general trend, showed the lowest performance with an R2 of 0.8710. These findings suggest that the Multi-layer Perceptron is the most accurate model for this dataset, followed by Gamma Regression and Support Vector Regression. This underscores the importance of careful model selection and optimization in regression analysis in combination with computational simulation of membrane processes.

NSEMBLE FINE TUNED MULTI LAYER PERCEPTRON FOR PREDICTIVE ANALYSIS OF WEATHER PATTERNS AND RAINFALL FORECASTING FROM SATELLITE DATA

The accurate prediction of weather patterns and rainfall forecasting is critical for various sectors, including agriculture, disaster management, and water resource planning. Traditional models often struggle to capture the complex interactions between atmospheric variables, particularly when integrating diverse types of satellite data (binary, categorical, and numerical). To address this challenge, an ensemble fine-tuned multi-layer perceptron (MLP) model is developed, combining the strengths of multiple machine learning techniques for more robust predictions. The primary problem is the difficulty in handling mixed data types while maintaining high prediction accuracy. Satellite data, including binary indicators (e.g., cloud presence), categorical features (e.g., cloud types), and numerical variables (e.g., temperature, humidity, and wind speed), provide rich information but require specialized processing for effective forecasting. The proposed method involves fine-tuning an ensemble of MLP models with backpropagation, dropout regularization, and batch normalization to reduce overfitting and enhance generalization. The ensemble integrates predictions from individual MLP models, each trained on different subsets of features (binary, categorical, numerical). This technique allows the model to leverage complementary strengths and produce more accurate rainfall forecasts. Satellite data is preprocessed and normalized before training, and categorical variables are one-hot encoded to ensure compatibility with the MLP architecture. Results from testing on historical satellite weather datasets demonstrate significant improvements in forecast accuracy. The ensemble MLP achieved an accuracy of 91.3%, with a precision of 90.7%, recall of 89.5%, and an F1-score of 90.1%. The model performed exceptionally well in identifying critical rainfall events, reducing false positives by 12% compared to traditional models.

Gut metagenome-derived image augmentation and deep learning improve prediction accuracy of metabolic disease classification.

In recent years, statistics and machine learning methods have been widely used to analyze the relationship between human gut microbial metagenome and metabolic diseases, which is of great significance for the functional annotation and development of microbial communities. In this study, we proposed a new and scalable framework for image enhancement and deep learning of gut metagenome, which could be used in the classification of human metabolic diseases. Each data sample in three representative human gut metagenome datasets was transformed into image and enhanced, and put into the machine learning models of logistic regression (LR), support vector machine (SVM), Bayesian network (BN) and random forest (RF), and the deep learning models of multilayer perceptron (MLP) and convolutional neural network (CNN). The accuracy performance of the overall evaluation model for disease prediction was verified by accuracy (A), accuracy (P), recall (R), F1 score (F1), area under ROC curve (AUC) and 10 fold cross-validation. The results showed that the overall performance of MLP model was better than that of CNN, LR, SVM, BN, RF and PopPhy-CNN, and the performance of MLP and CNN models was further improved after data enhancement (random rotation and adding salt-and-pepper noise). The accuracy of MLP model in disease prediction was further improved by 4%-11%, F1 by 1%-6% and AUC by 5%-10%. The above results showed that human gut metagenome image enhancement and deep learning could accurately extract microbial characteristics and effectively predict the host disease phenotype. The source code and datasets used in this study can be publicly accessed in https://github.com/HuaXWu/GM_ML_Classification.git.

Predictive modeling of flow characteristics in supersonic separators using machine learning

In the pursuit of cleaner energy sources, pre-processing fuel streams is crucial before distribution. Challenges arise during this process, leading to operational issues and diminished transmission efficiency. Among different technologies, supersonic separators are the ones that separate undesired components from the main gas stream more effectively. Precise forecasting of nozzle flow rate and shockwave location at a low cost can markedly enhance the efficiency of manufacturing and rating. In this research, the one-dimensional method has been developed to solve flow equations simultaneously by implementing equations of state. CFD has been used for the verification of the developed model, and the effect of different equations of state is investigated. Results show that the one-dimensional model significantly reduces the calculation time. Different EOSs result in up to 2.0 % and 3.5 % variation in the prediction of shock location and the mass flow rate, respectively.A machine learning methodology is employed to predict the location of shockwaves and the choked flow rate at an even lower cost. A dataset of 160 entries, generated using Peng Robinson's EOS, serves as the basis for this analysis. Prediction tasks are executed using Random Forest, k-nearest Neighbors, and Multilayer Perceptron models. While all three models demonstrate proficiency in data interpolation with R2 scores up to 0.9962 and 0.9951 for the shockwave location and mass flow rate, respectively, discerning their performance in extrapolation appears contingent on the specific case. In the case of extrapolation, Multilayer Perceptron and k-Nearest Neighbors show better match to one-dimensional results.

Predicting the microstructure of composite plates using deep learning based on thermal expansion

In this paper, we introduce a novel method to predict composite plate microstructures based on thermal expansion using deep learning. Traditional analysis methods are cumbersome and resource intensive. We created a detailed dataset from finite element simulations, capturing edge displacements during thermal expansion. This dataset feeds into our multilayer perceptron model, generating precise microstructure matrices. Our approach achieves high accuracy and efficiency, markedly reducing computational overhead. By combining deep learning with finite element analysis, we streamline predictions and enhance precision. This integrated approach serves as a potent tool for engineers and materials scientists, facilitating composite structure design and optimization.

Predicting creep behavior in composites from microstructural features using deep learning

This study uses a multilayer perceptron deep learning model to predict creep strain vs time curves in composite materials based on their microstructural features. Finite element simulations generate ground truth data for model training and validation. The multilayer perceptron model, trained on this comprehensive dataset, effectively captures the complex relationships between the microstructure and creep behavior, achieving high accuracy. Comparative analysis with traditional models shows the multilayer perceptron model’s superior performance. This demonstrates the model’s potential for reliable application in various engineering fields, offering improved predictions of composite material behavior under creep conditions.

Mathematical modeling of ions adsorption from water/wastewater sources via porous materials: A machine learning-based approach

This paper developed the predictive modeling of substance concentration (C) utilizing the input parameters x and y, for analysis of adsorption process. Employing three distinct machine learning models—Multilayer Perceptron (MLP), polynomial regression (PR), and Support Vector Machine (SVM)—the study investigates the efficacy of models in capturing the relationships between the inputs and output. The models are trained from data obtained from mass transfer calculations for removal of solute from solution via porous adsorbent. Furthermore, the hyper-parameters for each model are optimized through the utilization of the Political Optimizer (PO). The Multilayer Perceptron model emerges as a standout performer, showcasing an exceptional R-squared score of 0.9981, indicative of a robust fit to the data. Complemented by impressively low MAE and MSE values (7.94043E-01 and 2.0420E+00, respectively), the MLP model attests to its ability to provide accurate predictions and discern underlying patterns within the dataset. The polynomial regression model, while slightly trailing behind the MLP in terms of R-squared score (0.95929), revealed commendable predictive performance. Support Vector Machine also proves to be a formidable contender, boasting a robust R-squared score of 0.96055.

Multi-temperature capable enhanced bidirectional long short term memory-multilayer perceptron hybrid model for lithium-ion battery SOC estimation

In this study, we propose a novel hybrid modeling framework for State of Charge (SOC) estimation across a broad temperature spectrum. First, we build a hybrid model to optimize stacked layers of stacked bidirectional long short term memory networks by introducing dropout mechanisms. At the same time, we also optimize the traditional multi-layer perceptron model to ResMLP, which is improved by introducing residual linkage, and then integrate the two optimization models. Finally, the synergistic effect and attention mechanism of genetic algorithm and particle swarm optimization are used to optimize its parameters. We then rigorously tested the model on nine datasets, including HPPC, DST and BBDST, at different temperatures of 5 °C, 15 °C and 35 °C. Using MAE, RMSE and MAXE benchmarks, our research results show that the proposed hybrid model outperforms the benchmark algorithm, achieving significantly enhanced performance and higher accuracy, and the maximum SOC estimation error is kept below 4.53 %. In addition, experimental evaluation at different temperatures shows the robustness and adaptability of the proposed algorithm.

Clinical value of a radiomics model based on machine learning for the prediction of prostate cancer.

Radiomics models have demonstrated good performance for the diagnosis and evaluation of prostate cancer (PCa). However, there are currently no validated imaging models that can predict PCa or clinically significant prostate cancer (csPCa). Therefore, we aimed to identify the best such models for the prediction of PCa and csPCa. We performed a retrospective study of 942 patients with suspected PCa before they underwent prostate biopsy. MRI data were collected to manually segment suspicious regions of the tumor layer-by-layer. We then constructed models using the extracted imaging features. Finally, the clinical value of the models was evaluated. A diffusion-weighted imaging (DWI) plus apparent diffusion coefficient (ADC) random-forest model and a T2-weighted imaging plus ADC and DWI multilayer perceptron model were the best models for the prediction of PCa and csPCa, respectively. Areas under the curve (AUCs) of 0.942 and 0.999, respectively, were obtained for a training set. Internal validation yielded AUCs of 0.894 and 0.605, and external validation yielded AUCs of 0.732 and 0.623. Models based on machine learning comprising radiomic features and clinical indicators showed good predictive efficiency for PCa and csPCa. These findings demonstrate the utility of radiomic models for clinical decision-making.

Integrating machine learning models to learn potentially non-monotonic preferences for multi-criteria sorting from large-scale assignment examples

Learning preferences from assignment examples has attracted considerable attention in the field of multi-criteria sorting (MCS). However, traditional MCS methods, designed to infer decision makers’ preferences from small-scale assignment examples, encounter limitations when confronted with large-scale data sets. Additionally, the presence of decision makers’ non-monotonic preferences for certain criteria in MCS problems necessitates accounting for potential non-monotonicity when devising preference learning methods. To address this, this paper proposes some new models to learn potentially non-monotonic preferences for MCS problems from large-scale assignment examples by leveraging machine learning models. Specifically, we first introduce the Piecewise-Linear Neural Network (PLNN) model, which leverages the threshold-based value-driven sorting procedure as the underlying sorting model and integrates a perceptron-based model to establish piecewise-linear marginal value functions to approximate real ones. On this basis, we address MCS problems with criteria interactions and extend the PLNN model to develop the Piecewise-Linear Factorization Machine-based Neural Network (PLFMNN) model by incorporating the factorization machine to factorize interaction coefficients. Training these models allows us to learn potentially non-monotonic preferences of decision makers. To illustrate the proposed models, we apply them to a red wine quality classification problem. Furthermore, we assess the performance of the proposed models through computational experiments on both artificial and real-world data sets. Additionally, we conduct statistical tests to ascertain the significance of the performance differences. Experimental results reveal that the proposed models are comparable to the multilayer perceptron model and outperform other baseline models on most data sets, thus affirming their efficacy. Finally, we conduct some sensitivity analysis to assess the impact of certain parameters on the performance of the proposed models and compare them with existing studies from a theoretical perspective, further demonstrating their effectiveness.

Machine learning-driven survival prediction in gestational trophoblastic neoplasms: a focus on PSTT and ETT prognosis.

The clinicopathological characteristics and prognosis of placental site trophoblastic tumor (PSTT) and epithelioid trophoblastic tumor (ETT) have not been well summarized. Consequently, we conducted the largest to date series of samples of both types and employed machine learning (ML) to assess treatment effectiveness and develop accurate prognostic models for patients with GTN. Gestational choriocarcinoma (GCC) was used as the control group to show the clinical features of PTSS and ETT. The Surveillance, Epidemiology, and End Results (SEER) database provided the data used for this study's analysis. To identify the prognostic variables, we conducted Cox regression analysis and constructed prognostic models using five ML algorithms to predict the 5-year survival. A validation method incorporating the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to validate the accuracy and reliability of the ML models. We also investigated the role of multiple therapeutic options using the Kaplan-Meier survival analysis. The study population comprised 725 patients. Among them, 139 patients had ETT, 107 had PSTT, and 479 had GCC. There were no significant differences in survival between the different tumor groups. Multivariate Cox regression analysis revealed that metastasis was a significant prognostic factor for GCC, while older age and radiotherapy were significant prognostic factors for PTSS and ETT. ML models revealed that the Gradient Boosting classifier accurately predicted the outcomes, followed by the random forest classifier, K-Nearest Neighbors, Logistic Regression, and multilayer perceptron models. The most significant contributing factors were tumor size, year of diagnosis, age, and race. Our study provides a method for treatment and prognostic assessment of patients with GTN. The ML we developed can be used as a convenient individualized tool to facilitate clinical decision making.

Multiple remotely sensed datasets and machine learning models to predict chlorophyll-a concentration in the Nakdong River, South Korea.

The Nakdong River is a crucial water resource in South Korea, supplying water for various purposes such as potable water, irrigation, and recreation. However, the river is vulnerable to algal blooms due to the inflow of pollutants from multiple points and non-point sources. Monitoring chlorophyll-a (Chl-a) concentrations, a proxy for algal biomass is essential for assessing the trophic status of the river and managing its ecological health. This study aimed to improve the accuracy and reliability of Chl-a estimation in the Nakdong River using machine learning models (MLMs) and simultaneous use of multiple remotely sensed datasets. This study compared the performances of four MLMs: multi-layer perceptron (MLP), support vector machine (SVM), random forest (RF), and eXetreme Gradient Boosting (XGB) using three different input datasets: (1) two remotely sensed datasets (Sentinel-2 and Landsat-8), (2) standalone Sentinel-2, and (3) standalone Landsat-8. The results showed that the MLP model with multiple remotely sensed datasets outperformed other MLMs with 0.43 - 0.86 greater in R2 and 0.36 - 5.88 lower in RMSE. The MLP model demonstrated the highest performance across the range of Chl-a concentrations and predicted peaks above 20 mg/m3 relatively well compared to other models. This was likely due to the capacity of MLP to handle imbalanced datasets. The predictive map of the spatial distribution of Chl-a generated by MLP well captured the areas with high and low Chl-a concentrations. This study pointed out the impacts of imbalanced Chl-a concentration observations (dominated by low Chl-a concentrations) on the performance of MLMs. The data imbalance likely led to MLMs poorly trained for high Chl-a values, producing low prediction accuracy. In conclusion, this study demonstrated the value of multiple remotely sensed datasets in enhancing the accuracy and reliability of Chl-a estimation, mainly when using the MLP model. These findings would provide valuable insights into utilizing MLMs effectively for Chl-a monitoring.

PROTA: A Robust Tool for Protamine Prediction Using a Hybrid Approach of Machine Learning and Deep Learning

Protamines play a critical role in DNA compaction and stabilization in sperm cells, significantly influencing male fertility and various biotechnological applications. Traditionally, identifying these proteins is a challenging and time-consuming process due to their species-specific variability and complexity. Leveraging advancements in computational biology, we present PROTA, a novel tool that combines machine learning (ML) and deep learning (DL) techniques to predict protamines with high accuracy. For the first time, we integrate Generative Adversarial Networks (GANs) with supervised learning methods to enhance the accuracy and generalizability of protamine prediction. Our methodology evaluated multiple ML models, including Light Gradient-Boosting Machine (LIGHTGBM), Multilayer Perceptron (MLP), Random Forest (RF), eXtreme Gradient Boosting (XGBOOST), k-Nearest Neighbors (KNN), Logistic Regression (LR), Naive Bayes (NB), and Radial Basis Function-Support Vector Machine (RBF-SVM). During ten-fold cross-validation on our training dataset, the MLP model with GAN-augmented data demonstrated superior performance metrics: 0.997 accuracy, 0.997 F1 score, 0.998 precision, 0.997 sensitivity, and 1.0 AUC. In the independent testing phase, this model achieved 0.999 accuracy, 0.999 F1 score, 1.0 precision, 0.999 sensitivity, and 1.0 AUC. These results establish PROTA, accessible via a user-friendly web application. We anticipate that PROTA will be a crucial resource for researchers, enabling the rapid and reliable prediction of protamines, thereby advancing our understanding of their roles in reproductive biology, biotechnology, and medicine.