Improving Dendritic Neuron Model With Dynamic Scale-Free Network-Based Differential Evolution

Some recent research reports that a dendritic neuron model(DNM)can achieve better performance than traditional artificial neuron networks(ANNs)on classification,prediction,and other problems when its parameters are well-tuned by a learning algorithm.However,the back-propagation algorithm(BP),as a mostly used learning algorithm,intrinsically suffers from defects of slow convergence and easily dropping into local minima.Therefore,more and more research adopts non-BP learning algorithms to train ANNs.In this paper,a dynamic scale-free network-based differential evolution(DSNDE)is developed by considering the demands of convergent speed and the ability to jump out of local minima.The performance of a DSNDE trained DNM is tested on 14 benchmark datasets and a photovoltaic power forecasting problem.Nine meta-heuristic algorithms are applied into comparison,including the champion of the 2017 IEEE Congress on Evolutionary Computation(CEC2017)benchmark competition effective butterfly optimizer with covariance matrix adapted retreat phase(EBOwithCMAR).The experimental results reveal that DSNDE achieves better performance than its peers.

Prognostic role of artificial intelligence among patients with hepatocellular cancer:A systematic review

BACKGROUND Prediction of survival after the treatment of hepatocellular carcinoma(HCC)has been widely investigated,yet remains inadequate.The application of artificial intelligence(AI)is emerging as a valid adjunct to traditional statistics due to the ability to process vast amounts of data and find hidden interconnections between variables.AI and deep learning are increasingly employed in several topics of liver cancer research,including diagnosis,pathology,and prognosis.AIM To assess the role of AI in the prediction of survival following HCC treatment.METHODS A web-based literature search was performed according to the Preferred Reporting Items for Systemic Reviews and Meta-Analysis guidelines using the keywords“artificial intelligence”,“deep learning”and“hepatocellular carcinoma”(and synonyms).The specific research question was formulated following the patient(patients with HCC),intervention(evaluation of HCC treatment using AI),comparison(evaluation without using AI),and outcome(patient death and/or tumor recurrence)structure.English language articles were retrieved,screened,and reviewed by the authors.The quality of the papers was assessed using the Risk of Bias In Non-randomized Studies of Interventions tool.Data were extracted and collected in a database.RESULTS Among the 598 articles screened,nine papers met the inclusion criteria,six of which had low-risk rates of bias.Eight articles were published in the last decade;all came from eastern countries.Patient sample size was extremely heterogenous(n=11-22926).AI methodologies employed included artificial neural networks(ANN)in six studies,as well as support vector machine,artificial plant optimization,and peritumoral radiomics in the remaining three studies.All the studies testing the role of ANN compared the performance of ANN with traditional statistics.Training cohorts were used to train the neural networks that were then applied to validation cohorts.In all cases,the AI models demonstrated superior predictive performance compared with traditional statistics with significantly improved areas under the curve.CONCLUSION AI applied to survival prediction after HCC treatment provided enhanced accuracy compared with conventional linear systems of analysis.Improved transferability and reproducibility will facilitate the widespread use of AI methodologies.

Soft Sensors for Property‑Controlled Multi‑Stage Press Hardening of 22MnB5

In multi-stage press hardening,the product properties are determined by the thermo-mechanical history during the sequence of heat treatment and forming steps.To measure these properties and finally to control them by feedback,two soft sensors are developed in this work.The press hardening of 22MnB5 sheet material in a progressive die,where the material is first rapidly austenitized,then pre-cooled,stretch-formed,and finally die bent,serves as the framework for the development of these sensors.To provide feedback on the temporal and spatial temperature distribution,a soft sensor based on a model derived from the Dynamic mode decomposition(DMD)is presented.The model is extended to a parametric DMD and combined with a Kalman filter to estimate the temperature(-distribution)as a function of all process-relevant control vari-ables.The soft sensor can estimate the temperature distribution based on local thermocouple measurements with an error of less than 10°C during the process-relevant time steps.For the online prediction of the final microstructure,an artificial neural network(ANN)-based microstructure soft sensor is developed.As part of this,a transferable framework for deriving input parameters for the ANN based on the process route in multi-stage press hardening is presented,along with a method for developing a training database using a 1-element model implemented with LS-Dyna and utilizing the material model Mat248(PHS_BMW).The developed ANN-based microstructure soft sensor can predict the final microstructure for specific regions of the formed and hardened sheet in a time span of far less than 1 s with a maximum deviation of a phase fraction of 1.8%to a reference simulation.