Electronic Information Signal Recognition Based on a Stochastic Neural Network Algorithm by Jiaye Wang

“…With the continuous increase of L, the training error of SCN network and TSVD-SCN network will be reduced to very small, and the training error of TSVD-SCN network is also less than SCN. …”
Get full text

Train Neural Networks with a Hybrid Method That Incorporates a Novel Simulated Annealing Procedure by Ioannis G. Tsoulos, Vasileios Charilogis, Dimitrios Tsalikakis

“…The comparison with other neural network training techniques as well as the statistical comparison revealed that the proposed method is significantly superior, as it managed to significantly reduce the neural network training error in the majority of the used datasets.…”
Get full text

Antibacterial activity of donkey’s milk against clinical isolate of Klebsiella pneumoniae by Ljubiša Šarić, Lato Pezo, Jelena Krulj, Jelena Tomić, Dragana Plavšić, Pavle Jovanov, Milana Matić

“…The artificial neural network model for prediction of K. pneumoniae count gave acurate fit to experimental data, showing a reasonably good (overall r2 for donkey milk was 0.986, with training error 4.67∙10-4, while r2 for nutrient broth was 0.982 and training error was 0.002).…”
Get full text

AdaBoost algorithm based on fitted weak classifier by Pengfeng SONG, Qingwei YE, Zhihua LU, Yu ZHOU

“…AdaBoost algorithm was proposed to minimize the accuracy caused by weak classifiers by minimizing the training error rate,and the single threshold was weaker and difficult to converge.The AdaBoost algorithm based on the fitted weak classifier was proposed.Firstly,the mapping relationship between eigenvalues and marker values was established.The least squares method was introduced to solve the fitting polynomial function,and the continuous fitting values were converted into discrete categorical values,thereby obtaining a weak classifier.From the many classifiers obtained,the classifier with smaller fitting error was selected as the weak classifier to form a new AdaBoost strong classifier.The UCI dataset and the MIT face image database were selected for experimental verification.Compared with the traditional Discrete-AdaBoost algorithm,the training speed of the improved algorithm was increased by an order of magnitude.And the face detection rate can reach 96.59%.…”
Get full text

Optimizing PEMFC parameter identification using improved pufferfish algorithm and CNN by Ji Li, Changiz Bastani

“…The results with almost 2.5-unit training error for the PEMFC parameter using sample data during training provide a promising output for the system. …”
Get full text

Carbon emission prediction method of steel plants based on long short-term memory network by Fengyun LI, Zehui DOU, Peng LI, Wei GUO

“…As the second largest carbon emitter in China, iron and steel enterprises have great potential for carbon emission reduction.In order to facilitate the supervision and control of carbon emissions by relevant departments, carbon emission prediction research is carried out.Taking a steelmaking plant as the research object, firstly, the carbon dioxide emissions in the steelmaking process were analyzed, and 10 energy substances that caused carbon emissions were determined.The basic energy data of the steelmaking plant from 2001 to 2023 were collected, and the carbon emissions were calculated from the basic energy data according to the carbon emission accounting method.Secondly, based on the long short-term memory network to predict the carbon emissions in the next 7 years, the training error and test error were close to 0.01, and the actual error was 1 323 307.46 tons of carbon dioxide.Then, the Mann-Kendall trend test was used to evaluate the overall carbon emission trend of the steelmaking plant.Finally, some reasonable suggestions were put forward for steelmaking plants in order to actively respond to the goal of low-carbon environmental protection.…”
Get full text

College psychological stress assessment system based on LabVIEW and WTA integration by Ting Wang

“…The experimental results show that during the iterative training process, the training error of the research algorithm reaches 10–5 at the 200th iteration. …”
Get full text

Unsupervised Discretization of Continuous Variables in a Chicken Egg Quality Traits Dataset by Zeynel Cebeci, Figen Yıldız

“…We revealed that these unsupervised discretization methods can decrease the training error rates and increase the test accuracies of the classification tree models. …”
Get full text

Introducing an Evolutionary Method to Create the Bounds of Artificial Neural Networks by Ioannis G. Tsoulos, Vasileios Charilogis, Dimitrios Tsalikakis

“…The new method effectively constructs the parameter value range of the artificial neural network with one processing level and sigmoid outputs, both achieving a reduction in training error and preventing the network from experiencing overfitting phenomena. …”
Get full text

Rolling Bearing Fault Diagnosis Based on Stacked Autoencoder Network with Dynamic Learning Rate by Hong Pan, Wei Tang, Jin-Jun Xu, Maxime Binama

“…According to the positive and negative value of the training error gradient, a learning rate reducing strategy is designed in order to be consistent with the current operation of the network. …”
Get full text

XNODE: A XAI Suite to Understand Neural Ordinary Differential Equations by Cecília Coelho, Maria Fernanda Pires da Costa, Luís L. Ferrás

“…This reveals that a low training error does not necessarily equate to comprehensive understanding or accurate modelling of the underlying data dynamics.…”
Get full text

RUE: A robust personalized cost assignment strategy for class imbalance cost-sensitive learning by Shanlin Zhou, Yan Gu, Hualong Yu, Xibei Yang, Shang Gao

“…Traditional cost-sensitive learning approaches always solve CIL problem by assigning a constant higher training error penalty for all minority instances than that of majority instances, but ignore the significance of location information. …”
Get full text

A density-based MS disease diagnosis model using the capuchin search algorithm and an ensemble of deep neural networks by LiJuan Bai, Jiao Wu, Li Chen, Xin Jiang, ZhuYin Song

“…This means that in this process, the vector and topology of the deep neural network are adjusted using the CapSA algorithm in such a way that the training error is minimized. Finally, after creating the trained models, the weighted combination of the outputs of these three models is used for the final diagnosis. …”
Get full text

A Parameter-Optimized DBN Using GOA and Its Application in Fault Diagnosis of Gearbox by Jingbo Gai, Junxian Shen, He Wang, Yifan Hu

“…After that the learning rate and the number of batch learning in DBN which have great influence on the training error would be properly selected. At the same time, the optimal structure distribution of DBN is given through comparison. …”
Get full text

Methods of security situation prediction for industrial internet fused attention mechanism and BSRU by Xiangdong HU, Zhengguo TIAN

“…The security situation prediction plays an important role in balanced and reliable work for industrial internet.In the face of massive, high-dimensional and time-series data generated in the industrial production process, traditional prediction models are difficult to accurately and efficiently predict the network security situation.Therefore, the methods of security situation prediction for industrial internet fused attention mechanism and bi-directional simple recurrent unit (BSRU) were proposed to meet the real-time and accuracy requirements of industrial production.Each security element was analyzed and processed, so that it could reflect the current network state and facilitate the calculation of the situation value.One-dimensional convolutional network was used to extract the spatial dimension features between each security element and preserve the temporal correlation between features.The BSRU network was used to extract the time dimension features between the data information and reduced the loss of historical information.Meanwhile, with the powerful parallel capability of SRU network, the training time of model was reduced.Attention mechanism was introduced to optimize the correlation weight of BSRU hidden state to highlight strong correlation factors, reduced the influence of weak correlation factors, and realized the prediction of industrial internet security situation combining attention mechanism and BSRU.The comparative experimental results show that the model reduces the training time and training error by 13.1% and 28.5% than the model using bidirectional long short-term memory network and bidirectional gated recurrent unit.Compared with the convolutional and BSRU network fusion model without attention mechanism, the prediction error is reduced by 28.8% despite the training time increased by 2%.The prediction effect under different prediction time is better than other models.Compared with other prediction network models, this model achieves the optimization of time performance and uses the attention mechanism to improve the prediction accuracy of the model under the premise of increasing a small amount of time cost.The proposed model can well fit the trend of network security situation, meanwhile, it has some advantages in multistep prediction.…”
Get full text

Exploring the role of virtual reality in preparing emergency responders for mass casualty incidents by Alena Lochmannová

“…Although VR did not reduce temporal demand (p = 0.057), it showed potential for improving focus and precision in training. Error rates in triage were similar across both training methods (p = 0.882), indicating comparable performance levels in patient classification. …”
Get full text

Analysis and prediction of land use/land cover change in the Llanganates-Sangay Connectivity Corridor by 2030 by Luis Jonathan Jaramillo Coronel, Andrea Cecilia Mancheno Herrera, Adriana Catalina Guzmán Guaraca, Juan Gabriel Mollocana Lara

“…The ANN model was trained on five explanatory variables and LULC maps from 2018 and 2020, achieving a training error of 8.46 %. Predictive accuracy was assessed by comparing the simulated 2022 LULC map with the 2022 MapBiomas map, resulting in a Kappa coefficient of 0.95, indicating excellent predictive accuracy. …”
Get full text

Non-Contact Oxygen Saturation Estimation Using Deep Learning Ensemble Models and Bayesian Optimization by Andrés Escobedo-Gordillo, Jorge Brieva, Ernesto Moya-Albor

“…Thus, by leveraging Bayesian optimization for hyperparameter tuning and integrating a Bagging Ensemble, we achieved a significant reduction in the training error (bias), achieving a better generalization over the test set, and reducing the variance in comparison with the baseline model for SpO₂ estimation.…”
Get full text

Numerical solution of KdV-mKdV equation based on PINN and its improved method(基于PINN及其改进算法求解KdV-mKdV方程) by 栗雪娟(LI Xuejuan), 刘瑜欣(LIU Yuxin)

“…The results of numerical simulation of the 1+1-dimensional KdV-mKdV equation under different parameters show that the training error of the gPINN algorithm is reduced by one order of magnitude compared with the PINN algorithm when the number of configuration points is reduced by 2 orders of magnitude.…”
Get full text

An artificial intelligence approach to palaeogeographic studies: a case study of the Late Ordovician brachiopods of Laurentia by Akbar Sohrabi

“…Based on the training algorithm and after 146 periods, the training error decreased, but the validation error increased (Fig. 7). …”
Get full text