The field of academic research on corporate sustainability management has gained significant sophistication since the economic growth has been associated with innovation. In this paper, we are to show our research project that aims to build an artificial intelligence-based neurofuzzy inference system to be able to approximate company’s innovation performance, thus the sustainability innovation potential. For this we used an empirical sample of Hungarian processing industry’s large companies and built an adaptive neuro fuzzy inference system.
Scientists around the world agree that nowadays, science is facing severe challenges like poor peer-review system, replicability crisis, or locked science behind paywalls. The National Science Center addresses at least some of them by introducing procedures that promote integrity, ethics, social responsibility, transparency, and openness in science.
Accurate network fault diagnosis in smart substations is key to strengthening grid security. To solve fault classification problems and enhance classification accuracy, we propose a hybrid optimization algorithm consisting of three parts: anti-noise processing (ANP), an improved separation interval method (ISIM), and a genetic algorithm-particle swarm optimization (GA-PSO) method. ANP cleans out the outliers and noise in the dataset. ISIM uses a support vector machine (SVM) architecture to optimize SVM kernel parameters. Finally, we propose the GA-PSO algorithm, which combines the advantages of both genetic and particle swarm optimization algorithms to optimize the penalty parameter. The experimental results show that our proposed hybrid optimization algorithm enhances the classification accuracy of smart substation network faults and shows stronger performance compared with existing methods.