Actual Data Sets Research Articles

Optimal Estimation of Reliability Parameters for Modified Frechet-Exponential Distribution Using Progressive Type-II Censored Samples with Mechanical and Medical Data

The aim of this research is to estimate the parameters of the modified Frechet-exponential (MFE) distribution using different methods when applied to progressive type-II censored samples. These methods include using the maximum likelihood technique and the Bayesian approach, which were used to determine the values of parameters in addition to calculating the reliability and failure functions at time t. The approximate confidence intervals (ACIs) and credible intervals (CRIs) are derived for these parameters. Two bootstrap techniques of parametric type are provided to compute the bootstrap confidence intervals. Both symmetric loss functions such as the squared error loss (SEL) and asymmetric loss functions such as the linear-exponential (LINEX) loss are used in the Bayesian method to obtain the estimates. The Markov Chain Monte Carlo (MCMC) technique is utilized in the Metropolis–Hasting sampler approach to obtain the unknown parameters using the Bayes approach. Two actual datasets are utilized to examine the various progressive schemes and different estimation methods considered in this paper. Additionally, a simulation study is performed to compare the schemes and estimation techniques.

Research on Fault Diagnosis of Rotating Parts Based on Transformer Deep Learning Model

The rotating parts of large and complex equipment are key components that ensure the normal operation of the equipment. Accurate fault diagnosis is crucial for the safe operation of these systems. To simultaneously extract both local and global valuable fault feature information from key components of complex equipment, this study proposes a fault diagnosis network model, named MultiDilatedFormer, which is based on the fusion of transformer and multi-head dilated convolution. The newly designed multi-head dilated convolution module is sequentially integrated into the transformer-encoder architecture, constructing a feature extraction module where the complementary advantages of both components enhance overall performance. Firstly, the sample is expanded into a two-dimensional feature map and then input into the newly designed feature extraction module. Finally, the diagnostic output is performed by the designed patch feature fusion module and classifier module. Additionally, interpretability research is conducted on the proposed model, aiming to understand the decision-making mechanism of the model through visual analysis of the entire decision process. The experimental results on three different datasets indicate that the proposed model achieved high accuracy in fault diagnosis with relatively short data windows. The highest accuracy reached 97.95%, which was up to 10.97% higher than other models. Furthermore, the feasibility of the model is also verified in the actual dataset of the rotating parts of the injection molding machine. The excellent performance of the model on different datasets demonstrates its effectiveness in extracting comprehensive fault feature information and also proves its great potential in practical industrial applications.

Employing deep learning in crisis management and decision making through prediction using time series data in Mosul Dam Northern Iraq

Specifically, Iraq is threatened in its second-largest northern city, Mosul, by the collapse of the Mosul Dam due to problems at the root of the dam, causing a wave of floods that will cause massive loss of life, resources, and public property. The objective of this study is to effectively monitor the level of dam water by predicting the level of water held by the dam In anticipation of achieving flood stage and breaking the dam, and supporting its behavior through formation 14-day time series data to predict seven days later. Used six deep learning models (deep neural network (DNN), convolutional neural network (CNN), convolutional neural network long short-term memory (CNN-LSTM), CNN-LSTM-Skip and CNN-LSTM Skip Attention) that models were trained to predict the water level in the dam; these levels of being under surveillance and prepared In case of danger, alert people to potential flood threats depending on the dam’s water level. These time series were created from the actual data sets of the dam; it’s a fundamental historical reading for 13 years (1993–2006) of the water level stored in the Mosul dam and was adopted in coordination with the Iraqi Ministry of Water Resources and the Centre for Research on Dams and Water Resources at Mosul University. The methodology applied in this study shows the model’s performance efficiency and the prediction results’ low error rate. It also compares those practical results to determine and adopt the performance-efficient model and a lower error rate. The comparison of these results proved the accuracy of its results, and superior to the CNN-LSTM model, it has the highest ability to perform through high accuracy with MAE = 0.087153 and time steps = 0 s 196 ms/step and loss = 0.00067. The current study demonstrated the ability to predict the water level in Mosul Dam, which suffers from foundation problems and may collapse in the future. Therefore, the water level in the dam must be monitored accurately. It also aims to test the effectiveness of the six models proposed in this study after evaluating their performance and applying the prediction process within a scenario to obtain predictive values after 14 days. The results showed the practical effectiveness of the hybrid CNN-LSTM model in correctly and accurately obtaining predictive values within the integrated framework of the required scenario. The study concluded that it is possible to enhance the ability to monitor and identify the potential risk of Mosul Dam at an early stage, and it also allows for proactive crisis management and sound decision-making, thus mitigating the adverse effects of crises on public safety and infrastructure and reducing human losses in areas along the Tigris River.

DRFL: Dynamic-Recall Focal Loss for Image Classification and Segmentation

ABSTRACT The accuracy of neural networks heavily depends on numerous precisely annotated samples. But in actual datasets, the number of samples for each category varies greatly. In the dataset, some classes may have a large sample size, called majority classes, while others may have a small sample size, called minority classes. Training model with imbalanced data is often conducive to the majority classes, while unfair to minority classes. As a result, the outputs return good performance on majority classes and bad performance on minority classes. This article proposes a new loss function called dynamic-recall focal loss (DRFL), which can solve the problem of imbalanced data categories in image classification and medical segmentation tasks. The DRFL assigns different weight coefficient to the classes according to their dynamic recall based on focal loss. Experimental results have shown that the newly proposed loss function DRFL can effectively improve the classification and segmentation accuracy of two imbalanced datasets.

Machine learning based prediction for bulk porosity and static elastic modulus of Yungang Grottoes sandstone

In this work, four mainstream machine learning (ML) techniques are used to evaluate the bulk porosity and static elastic modulus of weathered Yungang Grottoes sandstone. Datasets are gathered from the experiments, which includes 432 groups effective experimental data including 8 inputs features. bulk porosity and static elastic modulus were considered as outputs to determine the weathering degrees of Yungang Grottoes sandstone. The 4 performance criteria were used to evaluate the ML models. Results demonstrate that the Artificial Neural Network (ANN) is the best-fitted models for estimating the bulk porosity and static elastic modulus compared to Multiple Linear Regression (MLR), Support Vector Regression (SVR), Gaussian Process Regression (GPR). The accuracy of the trained model for static elastic modulus is slightly higher than that of bulk porosity. The GPR and ANN model can accurately predict the bulk porosity and static elastic modulus in training stages. The ANN with multi-hidden layers developed is competent with high degree of precision and generalization ability for bulk porosity and static elastic modulus compared to other selected regression-based ML models (MLR, SVR, and GPR). The coefficient of determinations of ANN in the range of (0.9537–0.9641) during the testing stages is more stable and higher than that of (0.8883–0.9453) other built ML models. The prediction efficiency of pretrained ANN model was well adjusted for the actual and forecast datasets at the training and testing stages, and the error range was no more than 0.7% and 0.15 GPa at both stages of prediction for bulk porosity and static elastic modulus respectively. And the ANN based static elastic modulus prediction model’s error proportions significantly decreased and were confined to a modest range between + 10% and − 10%. The proposed surrogate models are valid for the bulk porosity ranging from 7 to 14% and the static elastic modulus ranging from 0.7 to 1.4 Gpa, which can be utilized for the accurate and fast prediction of the weathering degrees of Yungang Grottoes sandstone.

CLPr_in_ML: Cleft Lip and Palate Reconstructed Features with Machine Learning

Background: Cleft lip and palate are two of the most common craniofacial congenital malformations in humans. It influences tens of millions of patients worldwide. The hazards of this disease are multifaceted, extending beyond the obvious facial malformation to encompass physiological functions, oral health, psychological well-being, and social aspects. Objective: The primary objective of our study is to demonstrate the importance of imaging in detecting cleft lip and palate. By observing the morphological and structural abnormalities involving the lip and palate through imaging methods, this study aims to establish imaging as the primary diagnostic approach for this disease. Methods: In this work, we proposed a novel model to analyze unilateral complete cleft lip and palate after velopharyngeal closure and non-left lip and palate patients from the Department of Stomatology of Xuzhou First People's Hospital, Conical Beam CT (CBCT) images in silicon. In order to demonstrate the generalization, the simulated dataset was constructed using the random disturbance factor, which is from the actual dataset. We extracted several raw features from CBCT images in detail. Then, we proposed a novel feature reconstruction method, including six types of reconstructed factors, to reconstruct the existing features. Then, the reconstructed features weretrained with machine learning algorithms. Finally, the testing and independent data model was utilized to analyze the performance of this work. Results: By comparing different operator features, the min operator, max operator, average operator, and all operators can achieve good performances in both the testing set and the independent set. result: none Conclusion: With the different operator features, the majority of classification models, including Gradient Boosting, Hist Gradient Boosting, Multilayer Perceptron, lightGBM, and broadened learning, classification algorithms can get the well-performances in the selected reconstructed feature operators.

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.

A bivariate dependent degradation model based on artificial neural network supported stochastic process and Copula function

AbstractIn order to use the high ability of the artificial neural network (ANN) in data fitting, this paper introduces an ANN in stochastic process to describe the mean function for degradation modeling. Due to the fact that the existing method cannot handle the bivariate dependent degradation conditions, a bivariate dependent degradation model based on Copula function and ANN‐supported stochastic processes is proposed. Considering the random effects caused by individual difference, it is assumed that the unknown parameters in the stochastic processes and Copula functions are randomly distributed. Based on the maximum likelihood and moment estimation methods, a related statistical inference method for ANN training and parameter estimation is developed to use the bivariate dependent degradation model. An actual fatigue crack dataset is used to demonstrate the validity of the proposed method. The obtained results show that the dependent relationship between two degradation indicators should not be neglected, and it can be efficiently handled by the proposed method. Furthermore, the proposed degradation model can provide reliability and degradation intervals with enough precision due to the fact that it considers the random effects caused by individual difference.

Metaheuristic optimization scheme for quantum kernel classifiers using entanglement‐directed graphs

AbstractEntanglement is crucial for achieving quantum advantages. However, in the context of quantum machine learning, existing optimization strategies for generating quantum classifier circuits often result in unentangled circuits, indicating an underutilization of the entanglement effect needed to learn complex patterns. In this study, we proposed a novel metaheuristic approach—genetic algorithm—for designing a quantum kernel classifier that incorporates expressive entanglement. This classifier utilizes a loopless entanglement‐directed graph, where each directed edge represents the entanglement between the target and control qubits. The proposed method consistently outperforms classical and quantum baselines across various artificial and actual datasets, achieving improvements up to 32.4% and 17.5%, respectively, compared with the best model among all other baselines. Moreover, this method successfully reconstructs the hidden entanglement structures underlying artificial datasets. The results also demonstrate that the optimized circuits exhibit diverse entanglement variations across different datasets, indicating the versatility of the proposed approach.

Interpretable model for rockburst intensity prediction based on Shapley values-based Optuna-random forest

To address the limitation of traditional machine learning models in explaining the rockburst intensity prediction process, this study proposes an interpretable rockburst intensity prediction model. The model was developed using 350 sets of actual rockburst sample data to explore the impact of input metrics on the final rockburst intensity level. The collected data underwent pre-processing using the isolation forest algorithm and synthetic minority oversampling technique. The random forest model was optimized through 5-fold cross-validation and the Optuna framework, resulting in the establishment of an Optuna-random forest (Op-RF) model that generates decision rules through its internal decision tree, utilizing the properties of the random forest model. The model was further interpreted using the Shapley additive explanations algorithm, both locally and globally. The results demonstrate that the proposed model achieved an area under curve score of 0.984. In comparison to eight other machine learning models, the proposed Op-RF model demonstrated superior accuracy, precision, recall, and F1 score. The model provides a transparent explanation of the prediction process, linking impact characteristics to the final output. Additionally, a cloud deployment method for the rockburst intensity prediction model is provided and its effectiveness is demonstrated through engineering verification. The proposed model offers a new approach to the application of machine learning in rockburst intensity prediction.

Open-set Communication Scene Recognition Based on Metric Learning

Abstract In this paper, we study the problem of recognizing communication scenes under open set protocols, i.e., making the extracted features of communication scenes sufficiently separable under a suitable metric space. To achieve it, we design a feature fusion model based on a convolutional neural network to extract the features of communication scenes, and we take the real and imaginary parts of the channel frequency response as model inputs to extract the scene features sufficiently. Furthermore, in order to optimize the distribution of scene features in angular space, a Linear-Softmax loss function is designed to restrict the inter-class angles of extracted features to be larger than the intra-class angles by two hyperparameters. Meanwhile, a cosine distance-based communication scene recognition algorithm is designed to complete the communication scene recognition by calculating the cosine distance between features. Finally, it is tested on actual measurement data sets, and the experimental results show that compared with the state-of-the-art loss functions, the proposed Linear-Softmax loss function enables the learning of the maximum angular margin. The Linear-Softmax loss function combined with the proposed communication scenes recognition algorithm can complete the recognition of both closed-set and open-set test data, with a recognition accuracy as high as 97.28% on the closed-set test set and 85.65% on the open-set test set.

A depth graph attention-based multi-channel transfer learning network for fluid classification from logging data

Fluid classification is a fundamental task in the field of geological sciences to achieve effective reservoir characterization and hydrocarbon exploration. Traditional fluid classification methods are often limited by long processing times and an inability to capture complex relationships within the data. To address this issue, this paper proposes a novel deep learning approach—the Deep Graph Attention Multi-channel Transfer Learning Network (DGMT), aimed at improving the efficiency and accuracy of fluid classification from logging data. This model comprises three key components: a graph attention layer, a multi-channel feature extractor, and a transfer learning module. The graph attention layer is designed to handle spatial dependencies between different logging channels, enhancing classification accuracy by focusing on critical features. The multi-channel feature extractor integrates information from various data sources, ensuring comprehensive utilization of the rich information in logging data. The transfer learning module allows the model to transfer knowledge from pre-trained models of similar tasks, accelerating the training process and significantly improving the model's generalization ability and robustness. This feature enables the DGMT model to adapt to different geological environments and logging conditions, showing superior performance over traditional methods. To validate the effectiveness of the DGMT model, we conducted experiments on actual logging datasets containing multiple oil wells. The experimental results indicate that, compared to common machine learning algorithms and other deep learning methods, the DGMT model significantly improves in accuracy and other classification performance metrics.

Bivariate cubic normal distribution for non-Gaussian problems

Probabilistic models play critical role in various engineering fields. Numerous critical issues exist in probabilistic modeling, especially for non-Gaussian correlated random variables. However, conventional parameter-based bivariate distribution models are generally developed for specific types of random variables, limiting their flexibility and applicability. A flexible bivariate distribution model is proposed, in which the joint cumulative distribution function is derived with the probability equivalently expressed as the summation of three basic probabilities corresponding to simple functions. These three basic probabilities are calculated with the aid of univariate cubic normal distribution, and thus the proposed model is named as bivariate cubic normal (BCN) distribution. The proposed BCN distribution has been applied in modeling several common bivariate distributions and actual engineering datasets. Results show that the BCN model accurately models both typical theoretical distributions and the JCDF of practical datasets, offering a significant improvement over existing models. Furthermore, the proposed BCN distribution has been utilized in seismic reliability assessment and the calculation of the mean recurrence interval and hazard curve of hurricane wind speed and storm size. Results demonstrate that the BCN distribution excels in modeling and matching capabilities, proving its accuracy and effectiveness in civil engineering applications.

Machine Learning-based Human Resource Management Information Retrieval and Classification Algorithm

Efficient human resource management (HRM) is essential for company achievement in today’s fast-paced corporate world. Businesses must find effective ways to retrieve and categorise the ever-growing amount of HR-related knowledge. This work presents a new method for retrieving and classifying HRM data using machine learning. Modern natural language processing (NLP) and CNN methods are used by the algorithm we developed to handle unorganized human resources (HR) information, including worker records, job postings, and certificates. HR decision-making is facilitated by the system’s ability to derive insightful information from the information using sophisticated text mining and machine learning algorithms.The two main parts of the method are classification and data extraction. HR workers can more easily obtain the required knowledge thanks to information retrieval, which makes it possible to search HR data quickly and accurately. Contrarily, categorisation optimises the division of human resources information into pertinent classifications, including job positions, competencies, and achievement grades. We assess our algorithm’s effectiveness on various datasets from actual HR datasets. The outcomes show how well our strategy works to streamline HRM procedures, cut down on manual labour, and increase the precision of decisions. Furthermore, our technology is compatible with corporate human resources offices and educational settings because it complies with worldwide university requirements.This study belongs to the growing body of knowledge in HRM. It provides a useful tool for businesses looking to improve employee relations, simplify HR procedures, and attract and retain talent. The suggested method is a valuable resource for academics and businesses alike because of its versatility and compliance with global educational norms.

Pulp grade monitoring using binocular image through multi-scale feature cross-attention fusion network and saliency map constraint

Pulp grade is an important indicator for performance monitoring in froth flotation process. Previous studies have shown that the pulp grade can be predicted more accurately by using the stereo vision information of froth images. However, due to the low intra-class variation among froth images under the identical working conditions and the similarity in shape and texture among bubbles, it is challenging for current binocular image-based methods for grade prediction. Therefore, to accurately predict the key performance indicators in flotation process, a prediction model based on binocular image fusion is proposed in this paper. First, a calculation method of froth image saliency map is proposed, and the saliency map is used as a priori knowledge to guide the prediction model to learn the characteristics of the region of interest in the image. This measure aims to solve the problem of difficulty in grade prediction caused by low intra-class differences in froth images. Then, a multi-scale feature cross-attention fusion network is introduced, wherein the multi-scale features of the left and right views serve as attention mechanism gating signals to extract common feature from binocular image. After that, a pulp grade prediction model is developed based on Video Transformer Network. The results on actual industrial flotation datasets show that, compared with other pulp grade prediction methods, our proposed approach reduces the mean absolute error and root mean square error by 22.84% and 23.55%, respectively.

A multi-step mathematical model-based predictive strategy for software release timing during testing stage

Mathematically precise modeling is important to be established to accurately examine the quantitative relationship between software testing and software reliability. Software testing process is complex since it is concerned with various factors such as test case execution, defect debugging, tester expertise, test case selection, and so forth. For this reason, it is required to be meticulous in formulating the software testing process in a manner which is mathematically concise. The software release life cycle or sequential release timeline, referring to the process related to the development, testing and distribution of a software product comprises several critical stages, and the length of this particular life cycle reveals variations depending on different factors like the type of product, the intended use of it, industry security, general standards and compliance. One consideration software engineers have is related to the release date of the software so that future commitments about the software’s release time can be formulated beforehand. In view of these aspects, a multi-step strategy for predicting software release dates is proposed in the current study along with the following stages: firstly, the proposed technique selects the utmost reliability growth model that very well fits the observed test data halfway through the testing period, and then employs it to forecast the probable date of release. This technique entails approximating the unknown parameters of suitable Software Reliability Growth Models (SRGMs). Finally, the chosen SRGM is used to forecast the release date of the software under test by fitting it to available fault data. The proposed method is straightforward and applied to test on a total of ten actual datasets collected from the literature. The results of the proposed technique reveal that in the majority of the situations, nearly exact approximation of date of release can be made halfway through the testing period. Moreover, the proposed method’s performance is also compared to that of a number of previous strategies present in the literature. The outcomes obtained by our study demonstrate that the proposed strategy may be used to forecast the release date of software in practical situations.

Kurdish Sign Language Recognition Using Convolutional Neural Network (CNN)

The present study examines the obstacles encountered by the deaf population, with a specific emphasis on the increasing significance of sign language in facilitating effective communication. The main mode of communication for deaf persons is Sign Language (SL), which is a visual and expressive method of communicating meaning via facial expressions, hand motions, and body gestures. The objective of this project is to automate the identification of sign language in order to improve accessibility and decrease reliance on interpreters. Specifically, the work focused on constructing an alphabet recognition system for Kurdish Sign Language (KSL). Due to its many intricacies and resemblances to the Arabic script, KSL requires a strong recognition model. The proposed method utilizes Convolutional Neural Networks (CNN) with an actual dataset to accurately recognize both numerical values and alphabetic characters in the Korean Sign Language (KSL). Real-time operation allows for rapid recognition of hand gestures and subsequent provision of textual output. The collection comprises 132,000 hand pictures, including 33 alphabetica and (0-9) numeral signs. Significantly, the use of MediaPipe, a method for processing 3D images, greatly improves the efficiency of detection. Multiple methodologies were investigated, and the integration of Convolutional Neural Networks (CNN), TensorFlow, and MediaPipe resulted in a remarkable accuracy of 99.87% with negligible dropout rates. This study establishes a foundation for enhanced communication and independence for the deaf population, representing a notable advancement in the automation of sign language recognition.

Maximum subspace transferability discriminant analysis: A new cross-domain similarity measure for wind-turbine fault transfer diagnosis

In the field of fault transfer diagnosis, many approaches only focus on the distribution alignment and knowledge transfer between the source domain and target domain. However, most of these approaches ignore the precondition of whether this transfer task is transferable. Current mainstream transferability discrimination methods heavily depend on expert knowledge and are extremely vulnerable to the noise interference and variations in feature scale. This limits their applicability due to the intelligent requirements and complex industrial environment. To address the challenges mentioned previously, this paper introduces a novel cross-domain similarity measure called maximum subspace transferability discriminant analysis (MSTDA) with zero-label prior knowledge. MSTDA is comprised of a maximum subspace representation and a similarity measurement criterion. During the phase of maximum subspace representation, a new kernel-induced Hilbert space is designed to map the low-dimensional original samples into the high-dimensional space to maximize the separability of different faults and then solve the separable intrinsic fault features. Following that, a novel similarity measurement criterion that is resistant to variations in feature scale is developed. This criterion is based on the orthogonal bases of intrinsic feature subspaces. The mini-batch sampling strategy is used to ensure the timeliness of MSTDA. Finally, the experimental results on three cases, particularly in the actual wind turbine dataset, confirm that the proposed MSTDA outperforms other well-known similarity measure methods in terms of transferability evaluation. The related code can be downloaded from https://qinyi-team.github.io/2024/09/Maximum-subspace-transferability-discriminant-analysis.

Unit extended exponential distribution with applications

In this paper, the unit extended exponential distribution is presented. The extended exponential distribution is a two-parameter lifespan distribution that has been demonstrated to be very adaptable. Our goal is to convert this flexibility between the unit interval and the extended exponential distribution. Several different types of probability density functions, including decreasing, left-skewed, right-skewed, and unimodal, have been shown. However, the hazard rate function may take on various forms, including J-shaped, U-shaped, and bathtub shapes. A few statistical properties, such as moments, mean, variance, skewness, kurtosis, expectation-weighted moments, incomplete moments, conditional moments, mean deviation, Lorenz and Bonferroni curves, mean residual life, mean inactivity time, and various entropy measures, round out the theoretical section. The maximum likelihood technique is utilized to estimate model parameters using unit data. This study provides simulation data to evaluate the proposed strategy. Two applications utilizing actual data sets emphasize the significance of the new model compared to existing unit models.

A novel energy management of public charging stations using attention-based deep learning model

Electricity grids are complex systems that must balance the supply and demand of electricity in real-time. However, with the increasing adoption of electric vehicles (EVs), managing the grid’s stability has become more challenging. EV charging can cause spikes in electricity demand, leading to peak demand periods that strain the power grid’s infrastructure. With the help of load forecasting, this effect on the grid can be mitigated by predicting the charging demand of electric vehicles in advance. This will help utilities adjust their energy supply in real-time, ensuring enough energy is available to meet demand, and preventing overloads or under utilization of the grid. Moreover, the EV charging demand is influenced by a wide range of factors, including charging station locations, weather, and time of day. Therefore, advanced deep learning models are required to learn these complex relationships and identify patterns in EV charging demand, enabling utilities to make more informed decisions. In this research, an attention-based deep learning approach is proposed for more accurate prediction of EV load demand. This novel approach integrates attention mechanisms with traditional deep learning models like LSTM and GRU, allowing the model to dynamically weight the importance of different features and focus on the most relevant information. The outcomes are compared to conventional deep learning and machine learning algorithms. To test the efficacy of the proposed framework, an actual ACN dataset for public EV charging stations is utilized.