Background Electroencephalography (EEG) signal analysis is a rapid, low-cost, and practical method for diagnosing the early stages of dementia, including mild cognitive impairment (MCI) and Alzheimer’s disease (AD). The extraction of appropriate biomarkers to assess a subject’s cognitive impairment has attracted a lot of attention in recent years. The aberrant progression of AD leads to cortical detachment. Due to the interaction of several brain areas, these disconnections may show up as abnormalities in functional connectivity and complicated behaviors. Methods This work suggests a novel method for differentiating between AD, MCI, and HC in two-class and three-class classifications based on EEG signals. To solve the class imbalance, we employ EEG data augmentation techniques, such as repeating minority classes using variational autoencoders (VAEs), as well as traditional noise-addition methods and hybrid approaches. The power spectrum density (PSD) and temporal data employed in this study’s feature extraction from EEG signals were combined, and a support vector machine (SVM) classifier was used to distinguish between three categories of problems. Results Insufficient data and unbalanced datasets are two common problems in AD datasets. This study has shown that it is possible to generate comparable data using noise addition and VAE, train the model using these data, and, to some extent, overcome the aforementioned issues with an increase in classification accuracy of 2 to 7%. Conclusion In this work, using EEG data, we were able to successfully detect three classes: AD, MCI, and HC. In comparison to the pre …

Superplasticizers (SPs), also known as naturally high-water reducers, are substances used to create high-strength concrete. Due to the system’s complexity, predicting concrete’s compressive strength can be difficult. In this study, a prediction model for the compressive strength with SP was developed to handle the high-dimensional complex non-linear relationship between the mixing design of SP and the compressive strength of concrete. After performing a statistical analysis of the dataset, a correlation analysis was performed and then 16 supervised machine learning regression techniques were used. Finally, by using the Extra Trees method and creating the SP variable values, it was shown that the compressive strength values of concrete increased with the addition of SP in the optimal dose. The results indicate that superplasticizers can often reduce the water content of concrete by 25 to 35 per cent and consequently resistivity increased by 50 to 75 per cent and the optimum amount of superplasticizers was up to 12 kg per cubic meter as well. From one point, the increase in superplasticizers does not lead to a rise in the concrete compressive strength, and it remains constant. According to the findings, SP additive has the most impact on concrete’s compressive strength after cement. Given the scant information now available on concrete-including superplasticizer, it is prudent to design a concrete mixing plan for future studies. It is also conceivable to investigate how concrete’s compressive strength is impacted by water reduction.