Traditional Models In Terms Research Articles

The Construction of “Interactive” English Translation Teaching Mode in the Background of Internet

Abstract This paper discusses the construction of “interactive” English translation teaching mode in the background of the Internet, and analyzes the application of neural machine translation model in improving the effect of translation teaching. The research adopts Word2vec algorithm to train word vectors, combines neural network language model and encoder-decoder structure, and constructs Transformer model and CNN+Transformer model to improve translation efficiency and quality. The translation experiments on sentences of different lengths show that the models perform better in short sentence translation. The experimental results show that the proposed model outperforms the traditional model in terms of BLEU value, and the best translation effect is found in test set 1, where the BLEU value is improved by 10.6 and 11.7 compared with that of the baseline model and the CNN model, respectively. The “interactive” teaching mode designed with POA theory significantly outperforms the traditional teaching mode regarding students’ lexical, grammatical and semantic translation quality. The “interactive” English translation teaching with neural translation model can effectively improve the quality of translation teaching and students’ learning effect.

Log-Kumaraswamy distribution: its features and applications

This article aimed to present a new continuous probability density function for a non-negative random variable that serves as an alternative to some bounded domain distributions. The new distribution, termed the log-Kumaraswamy distribution, could faithfully be employed to compete with bounded and unbounded random processes. Some essential features of this distribution were studied, and the parameters of its estimates were obtained based on the maximum product of spacing, least squares, and weighted least squares procedures. The new distribution was proven to be better than traditional models in terms of flexibility and applicability to real-life data sets.

A scalable approach for short-term disease forecasting in high spatial resolution areal data.

Short-term disease forecasting at specific discrete spatial resolutions has become a high-impact decision-support tool in health planning. However, when the number of areas is very large obtaining predictions can be computationally intensive or even unfeasible using standard spatiotemporal models. The purpose of this paper is to provide a method for short-term predictions in high-dimensional areal data based on a newly proposed "divide-and-conquer" approach. We assess the predictive performance of this method and other classical spatiotemporal models in a validation study that uses cancer mortality data for the 7907 municipalities of continental Spain. The new proposal outperforms traditional models in terms of mean absolute error, root mean square error, and interval score when forecasting cancer mortality 1, 2, and 3 years ahead. Models are implemented in a fully Bayesian framework using the well-known integrated nested Laplace estimationtechnique.

A Prior Knowledge-Guided Graph Convolutional Neural Network for Human Action Recognition in Solar Panel Installation Process

Human action recognition algorithms have garnered significant research interest due to their vast potential for applications. Existing human behavior recognition algorithms primarily focus on recognizing general behaviors using a large number of datasets. However, in industrial applications, there are typically constraints such as limited sample sizes and high accuracy requirements, necessitating algorithmic improvements. This article proposes a graph convolution neural network model that combines prior knowledge supervision and attention mechanisms, designed to fulfill the specific action recognition requirements for workers installing solar panels. The model extracts prior knowledge from training data, improving the training effectiveness of action recognition models and enhancing the recognition reliability of special actions. The experimental results demonstrate that the method proposed in this paper surpasses traditional models in terms of recognizing solar panel installation actions accurately. The proposed method satisfies the need for highly accurate recognition of designated person behavior in industrial applications, showing promising application prospects.

Cure rate models for heterogeneous competing causes.

Cure rate models have been widely studied to analyze time-to-event data with a cured fraction of patients. In this type of model, the number of concurrent causes is assumed to be a random variable. However, in practice, it is natural to admit that the distribution of the number of competing causes is different from individual to individual. Our proposal is to assume that the number of competing causes belongs to a class of a finite mixture of competing causes distributions. We assume the number of malignant cells follow a mixture of two power series distributions and suppose that the time to the event of interest follows a Weibull distribution. We consider the proportion of the cured number of competing causes depending on covariates, allowing direct modeling of the cure rate. The proposed model includes several well-known models as special cases and defines many new special models. An expectation-maximization algorithm is proposed for parameter estimation, where the expectation step involves the computation of the expected number of concurrent causes for each individual. A Monte Carlo simulation is performed to assess the behavior of the estimation method. In order to show the potential for the practice of our model, we apply it to the real medical data set from a population-based study of incident cases of cutaneous melanoma diagnosed in the state of São Paulo, Brazil, illustrating that the model proposed can outperform traditional models in terms of model fitting.

Traffic Flow Prediction Based on Hybrid Deep Learning Models Considering Missing Data and Multiple Factors

In the case of missing data, traffic forecasting becomes challenging. Many existing studies on traffic flow forecasting with missing data often overlook the relationship between data imputation and external factors. To address this gap, this study proposes two hybrid models that incorporate multiple factors for predicting traffic flow in scenarios involving data loss. Temperature, rainfall intensity and whether it is a weekday will be introduced as multiple factors for data imputation and forecasting. Predictive mean matching (PMM) and K-nearest neighbor (KNN) can find the data that are most similar to the missing values as the interpolation value. In the forecasting module, bidirectional long short-term memory (BiLSTM) network can extract bidirectional time series features, which can improve forecasting accuracy. Therefore, PMM and KNN were combined with BiLSTM as P-BiLSTM and K-BiLSTM to forecast traffic flow, respectively. Experiments were conducted using a traffic flow dataset from the expressway S6 in Poland, considering various missing scenarios and missing rates. The experimental results showed that the proposed models outperform other traditional models in terms of prediction accuracy. Furthermore, the consideration of whether it is a working day further improves the predictive performance of the models.

Gaussian Process Regression and Cooperation Search Algorithm for Forecasting Nonstationary Runoff Time Series

In the hydrology field, hydrological forecasting is regarded as one of the most challenging engineering tasks, as runoff has significant spatial–temporal variability under the influences of multiple physical factors from both climate events and human activities. As a well-known artificial intelligence tool, Gaussian process regression (GPR) possesses satisfying generalization performance but often suffers from local convergence and sensitivity to initial conditions in practice. To enhance its performance, this paper investigates the effectiveness of a hybrid GPR and cooperation search algorithm (CSA) model for forecasting nonstationary hydrological data series. The CSA approach avoids the premature convergence defect in GPR by effectively determining suitable parameter combinations in the problem space. Several traditional machine learning models are established to evaluate the validity of the proposed GPR-CSA method in three real-world hydrological stations of China. In the modeling process, statistical characteristics and expert knowledge are used to select input variables from the observed runoff data at previous periods. Different experimental results show that the developed GPR-CSA model can accurately predict nonlinear runoff and outperforms the developed traditional models in terms of various statistical indicators. Hence, a CSA-trained GPR model can provide satisfying training efficiency and robust simulation performance for runoff forecasting.

Metaheuristic Optimization of Random Forest for Predicting Punch Shear Strength of FRP-Reinforced Concrete Beams.

Predicting the punching shear strength (PSS) of fiber-reinforced polymer reinforced concrete (FRP-RC) beams is a critical task in the design and assessment of reinforced concrete structures. This study utilized three meta-heuristic optimization algorithms, namely ant lion optimizer (ALO), moth flame optimizer (MFO), and salp swarm algorithm (SSA), to select the optimal hyperparameters of the random forest (RF) model for predicting the punching shear strength (PSS) of FRP-RC beams. Seven features of FRP-RC beams were considered as inputs parameters, including types of column section (TCS), cross-sectional area of the column (CAC), slab's effective depth (SED), span-depth ratio (SDR), compressive strength of concrete (CSC), yield strength of reinforcement (YSR), and reinforcement ratio (RR). The results indicate that the ALO-RF model with a population size of 100 has the best prediction performance among all models, with MAE of 25.0525, MAPE of 6.5696, R2 of 0.9820, and RMSE of 59.9677 in the training phase, and MAE of 52.5601, MAPE of 15.5083, R2 of 0.941, and RMSE of 101.6494 in the testing phase. The slab's effective depth (SED) has the largest contribution to predicting the PSS, which means that adjusting SED can effectively control the PSS. Furthermore, the hybrid machine learning model optimized by metaheuristic algorithms outperforms traditional models in terms of prediction accuracy and error control.

An MRA Based MLR Model for Forecasting Indian Annual Rainfall Using Large Scale Climate Indices

A novel method for rainfall forecasting has been proposed using Multi Resolution Analysis (MRA). This approach decomposes annual rainfall series and long-term climate indices into component sub-series at different temporal scales, allowing for a more detailed analysis of the factors influencing annual rainfall. Multiple Linear Regression (MLR) is then used to predict annual rainfall, with climate indices sub-series as predictive variables, using a step-wise linear regression algorithm. The proposed model has been tested on Indian annual rainfall data and compared with the traditional MLR model. Results show that the MRA-based model outperforms the traditional model in terms of relative absolute error and correlation coefficient metrics. The proposed method offers several advantages over traditional methods as it can identify underlying factors affecting annual rainfall at different temporal scales, providing more accurate and reliable rainfall forecasts for better water resource management and agricultural planning. In conclusion, the MRA-based approach is a promising tool for improving the accuracy of annual rainfall predictions, and its implementation can lead to better water resource management and agricultural planning.

Research on Short Video Hotspot Classification Based on LDA Feature Fusion and Improved BiLSTM

Short video hot spot classification is a fundamental method to grasp the focus of consumers and improve the effectiveness of video marketing. The limitations of traditional short text classification are sparse content as well as inconspicuous feature extraction. To solve the problems above, this paper proposes a short video hot spot classification model combining latent dirichlet allocation (LDA) feature fusion and improved bi-directional long short-term memory (BiLSTM), namely the LDA-BiLSTM-self-attention (LBSA) model, to carry out the study of hot spot classification that targets Carya cathayensis walnut short video review data under the TikTok platform. Firstly, the LDA topic model was used to expand the topic features of the Word2Vec word vector, which was then fused and input into the BiLSTM model to learn the text features. Afterwards, the self-attention mechanism was employed to endow different weights to the output information of BiLSTM in accordance with the importance, to enhance the precision of feature extraction and complete the hot spot classification of review data. Experimental results show that the precision of the proposed LBSA model reached 91.52%, which is significantly improved compared with the traditional model in terms of precision and F1 value.

Activity Graph Based Convolutional Neural Network for Human Activity Recognition Using Acceleration and Gyroscope Data

Human activity recognition (HAR) using smartphone sensors have been recently studied in various applications including healthcare, fitness, and smart home. Their recognition accuracy often depends on high-quality feature design and effectiveness of classification algorithms, where existing work mostly replies on laborious hand-crafted design and shallow feature learning architecture. Recent deep learning techniques demonstrate outstanding effectiveness in performing automatic feature learning and outperform traditional models in terms of accuracy. But their performance is limited by the quality and volumes of available labelled data. It is challenging to achieve accurate multisubject HAR with only smartphone sensing data. This article proposes a novel optimal activity graph generation model incorporating a deep learning framework for automatic and accurate HAR with multiple subjects using only acceleration and gyroscope data. The activity graph generation model presents a multisensory integration mechanism with three-steps sorting algorithms for producing optimal activity graphs containing alignments of neighbored signals in their width and height. Then, we propose a deep convolutional neural network to automatically learn distinguishable features from the graphs for HAR. By leveraging superior presentation of correlations between human activities and neighbored signals alignments via optimal activity graphs, the learned features are endowed with more discriminative power. The experimental evaluation was carried out on several benchmark datasets (i.e., UCI, USCHAD, and UTD-MHAD). The results showed that our approach improved the average recognition accuracy by about 5% when compared with other state-of-the-art HAR methods. Particularly towards multisubject HAR cases (UTD-MHAD dataset with 21 subjects), it achieved up to 10% accuracy gain over other methods. These improvements show the advantage and potential of our method dealing with complex HAR problems with multiple subjects using limited sensing data.

Defect Synthesis Using Latent Mapping Adversarial Network for Automated Visual Inspection

In Industry 4.0, internet of things (IoT) technologies are expanding and advanced smart factories are currently being developed. To build an automated visual inspection (AVI) and achieve smartization of steel manufacturing, detecting defects in products in real-time and accurately diagnosing the quality of products are essential elements. As in various manufacturing industries, the steel manufacturing process presents a class imbalance problem for products. For example, fewer defect images are available than normal images. This study developed a new image synthesis methodology for the steel manufacturing industry called a latent mapping adversarial network. Inspired by the style-based generative adversarial network (StyleGAN) structure, we constructed a mapping network for the latent space, which made it possible to compose defect images of various sizes. We discovered the most suitable loss function, and optimized the proposed method in terms of convergence and computational cost. The experimental results demonstrate the competitive performance of the proposed model compared to the traditional models in terms of classification accuracy of 92.42% and F-score of 93.15%. Consequently, the problem of data imbalance is solved, and higher productivity in steel products is expected.

AI-Based Resource Allocation Techniques in Wireless Sensor Internet of Things Networks in Energy Efficiency with Data Optimization

For the past few years, the IoT (Internet of Things)-based restricted WSN (Wireless sensor network) has sparked a lot of attention and progress in order to attain improved resource utilisation as well as service delivery. For data transfer between heterogeneous devices, IoT requires a stronger communication network and an ideally placed energy-efficient WSN. This study uses deep learning architectures to provide a unique resource allocation method for wireless sensor IoT networks with energy efficiency as well as data optimization. EE (Energy efficiency) and SE (spectral efficiency) are two competing optimization goals in this case. The network’s energy efficiency has been improved because of a deep neural network based on whale optimization. The heuristic-based multi-objective firefly algorithm was used to optimise the data. This proposed method is applied to optimal power allocation and relay selection. The study is for a cooperative multi-hop network topology. The best resource allocation is achieved by reducing overall transmit power, and the best relay selection is accomplished by meeting Quality of Service (QoS) standards. As a result, an energy-efficient protocol has been created. The simulation results demonstrate the suggested model’s competitive performance when compared to traditional models in terms of throughput of 96%, energy efficiency of 95%, QoS of 75%, spectrum efficiency of 85%, and network lifetime of 91 percent.

Injury severity assessment of rear-end crashes via approaches based on generalized estimating equations

Rear-end crashes constitute the predominant type of crashes on highways and may lead to severe injuries and high property damage. Available statistical models primarily focus on injury severity and analyze potential factors that affect it. However, rear-end crashes may also be potentially correlated to vehicle, roadway, environmental, temporal, spatial, traffic, and crash characteristics. Additionally, unobserved heterogeneity regarding the effects may be present, which may be different in different crashes. In this context, multiple generalized estimating equation (GEE)-based models, developed using different working matrices and distributions, are proposed in this study to examine factors that affect injury severity. The proposed models account for both crash-related correlations and unobserved heterogeneity, thereby outperforming traditional models in terms of prediction accuracy. Among the explanatory variables considered in this study, the passenger car, minibus, curvature ratio, rainy weather, foggy weather, early morning, Thursday, autumn, winter, and average annual daily traffic volume were identified as contributing factors. However, significant differences were observed between the elasticity effects measured by different models, especially in terms of minibus and foggy weather. Thus, this study verifies that GEE-based models account for a greater amount of unobserved heterogeneity, yield better performance in terms of precision, and exhibit more consistent explanatory power compared to traditional models.

Fractal Model of Contact Thermal Stiffness

The continuity, self-similarity, and self-affinity of a microscopic contact surface can be described by the Weierstrass–Mandelbrot (W–M) function in fractal theory. To address the problems that the existing normal contact load fractal model does not take into account the effect of thermal stress and is not applicable to the temperature variation in the joint surface of the giant magnetostrictive ultrasonic vibration systems, a fractal model of thermal–elastic–plastic contact normal load fractal is established based on fractal theory. The model is an extension of the traditional model in terms of basic theory and application scope, and it takes into account the effects of temperature difference, linear expansion coefficient, fractal dimension, and other parameters. Finally, the effect of the temperature difference at the joint surface on the normal load of the thermoelastic contact is revealed through numerical simulations. The results show that the nonlinearity of the contact stiffness of the thermoelastic joint surface is mainly related to the surface roughness and the fractal dimension, while the effect of the temperature change on the joint surface properties within a certain range is linear.

Machine Learning-Based Intelligent Scoring System for English Essays under the Background of Modern Information Technology.

This work is to reduce the workload of teachers in English teaching and improve the writing level of students, so as to provide a way for students to practice English composition scoring independently and satisfy the needs of college teachers and students for intelligent English composition scoring and intelligently generated comments. In this work, it firstly clarifies the teaching requirements of college English classrooms and expounds the principles and advantages of machine learning technology. Secondly, a three-layer neural network model (NNM) is constructed by using the multilayer perceptron (MLP), combined with the latent Dirichlet allocation (LDA) algorithm. Furthermore, three semantic representation vector technologies, including word vector, paragraph vector, and full-text vector feature, are used to represent the full-text vocabulary of English composition. Then, a model based on the K-nearest neighbors (kNN) algorithm is proposed to generate English composition evaluation, and a final score based on the extreme gradient boosting (XGBoost) model is proposed. Finally, a model dataset is constructed using 800 college students' English essays for the CET-4 mock test, and the model is tested. The research results show that the semantic representation vector technology proposed can more effectively extract the lexical semantic features of English compositions. The XGBoost model and the kNN algorithm model are used to score and evaluate English compositions, which improves the accuracy of the scores. This makes the management of the entire scoring model more efficient and more accurate. It means that the model proposed is better than the traditional model in terms of evaluation accuracy. This work provides a new direction for the application of artificial intelligence technology in English teaching under the background of modern information technology.

Study the influence of adding fins to the plate of the solar collector on thermal performance under natural phenomena

To date, most of previous models related to adding fins inside Flat Plate Solar Collector (FPSC) have been carried out under steady-state heat flux and using active-methods for circulating the operating fluid. Therefore, the main purpose of this work is to investigate the thermal response of two developed finned-FPSC models under transient heat flux conditions with using passive systems. The first developed model (model-A-) include straight fins, while the second model with wavy fins (model-B-). Experimental tests are conducted in Baghdad – Iraq, while the numerical results are obtained by using Ansys Fluent CFD. The results indicated that the difference between numerical results and experimental findings is 8.12% for the average water temperature in a tank, while the operating fluid temperature at the exit of the rising pipe is about 8.36%. Furthermore, all new models outperform traditional models in terms of output performance. Moreover, the mass flow rate of model -B- is higher than that of model-A- and traditional models. (Model -B-) was found to be the best model among all others under the current study. The thermal performance of (model -B-) was found to be 16.7% higher than the traditional model in terms of water temperature inside the tank, while the operating fluid temperature at the outlet was increased by 16.2%. In addition, the overall thermal efficiency of the collector for the enhanced (model-B-) is increased by about 20.8% compared to the traditional model. The improved transient model (model-B-) presented in this work shall add a better enhancement to the FPSC behavior of the passive systems.

Hybrid optimization approach for optimal switching loss reduction in three-phase VSI

ABSTRACT In power electronics, the alleviation of the converter losses is the major target to accomplish higher efficiency and lower thermal stress that can pave the way to lifetime enhancement of devices. Hence, this paper intends to implement a novel variable switching frequency system for switching loss minimisation in a 3-phase Voltage Source Inverter (VSI). Here, the count of commutations is minimised by varying the switching frequency over the fundamental period. Here, the switching loss is reduced by optimising the modulation index and the reference angle of VSI with a novel hybrid optimisation model. The proposed novel hybrid optimisation model is constructed by hybridising the concept of Group search Algorithm (GSO) and Rider Optimisation Algorithm (ROA) and hence referred to as Bypass updated Group search Algorithm (BU-GSO). Finally, the performance of BU-GSO is evaluated over the traditional models in terms of convergence analysis, Total Harmonic Distortion (THD) as well. Moreover, the evaluation is accomplished with inductive load variation and restive load variation, respectively.

Fuzzy C means Based Evaluation Algorithms For Cancer Gene Expression Data Clustering

The influx of data in bioinformatics is primarily in the form of DNA, RNA, and protein sequences. This condition places a significant burden on scientists and computers. Some genomics studies depend on clustering techniques to group similarly expressed genes into one cluster. Clustering is a type of unsupervised learning that can be used to divide unknown cluster data into clusters. The k-means and fuzzy c-means (FCM) algorithms are examples of algorithms that can be used for clustering. Consequently, clustering is a common approach that divides an input space into several homogeneous zones; it can be achieved using a variety of algorithms. This study used three models to cluster a brain tumor dataset. The first model uses FCM, which is used to cluster genes. FCM allows an object to belong to two or more clusters with a membership grade between zero and one and the sum of belonging to all clusters of each gene is equal to one. This paradigm is useful when dealing with microarray data. The total time required to implement the first model is 22.2589 s. The second model combines FCM and particle swarm optimization (PSO) to obtain better results. The hybrid algorithm, i.e., FCM–PSO, uses the DB index as objective function. The experimental results show that the proposed hybrid FCM–PSO method is effective. The total time of implementation of this model is 89.6087 s. The third model combines FCM with a genetic algorithm (GA) to obtain better results. This hybrid algorithm also uses the DB index as objective function. The experimental results show that the proposed hybrid FCM–GA method is effective. Its total time of implementation is 50.8021 s. In addition, this study uses cluster validity indexes to determine the best partitioning for the underlying data. Internal validity indexes include the Jaccard, Davies Bouldin, Dunn, Xie–Beni, and silhouette. Meanwhile, external validity indexes include Minkowski, adjusted Rand, and percentage of correctly categorized pairings. Experiments conducted on brain tumor gene expression data demonstrate that the techniques used in this study outperform traditional models in terms of stability and biological significance.

Study of Various Classification Approaches including Deep Learning in Heart Disease Prediction

During the last couple of decades, apart from other diseases, heart disease has proved to be a major cause of human death. This refers to a wide variety of heart conditions which include diseased vessels, structural problems and blood clots. This paper is aimed to make an effective prediction about the vulnerability to heart disease of a person depending on the given health parameters at a very early stage & to reduce premature death, with an improved accuracy and reliability compared to other traditional models. Our proposed prediction model has proved to be reliable in this connection and has yielded a maximum accuracy of 99.0% using Random Forest, Support vector machine and Decision trees and K-nearest neighbour algorithms. Using Cross Validation, we have also prepared the model (with the highest accuracy level of 98.6%) to work efficiently, taking the correct pattern of the dataset. Further, the proposed model has outperformed other traditional models in terms of accuracy using some other algorithms (Hybrid Ensemble model, Extreme Gradient Boosting with Random Forest and Stochastic Gradient Descent with accuracies of 94.2%, 87%, 78% respectively). Moreover, TensorFlow has also been used in order to get reliable prediction of heart disease with an approximate accuracy of 83.44%. The novelty of our proposed model lies in showcasing better results compared to those obtained by the traditional models and this model can easily be applied in medical science to provide better diagnosis.