A major concern in smart power grids is when malicious or manipulated data is injected into measurement data due to malicious activities. Several approaches have been investigated to counter such false data injection attacks (FDIAs). However, such data-driven detectors present two major limitations. First, they neglect capturing the grid’s spatial characteristics. Second, they offer limited attack identification to familiar types of FDIAs since they are present within the model’s train sets. To conquer such limitations, we propose the use of an artificial intelligence-based graph autoencoder (GAE) for FDIAs detection. Our proposed detector offers three main advantages compared to existing detectors. First, it employs the operation of graph convolution to apprehend the grid’s spatial characteristics. Second, it offers an unsupervised autoencoder-based anomaly detection that requires only benign samples under normal operation for training. Third, it outperforms existing detectors by 16–47
Group imbalance data often occur in high dimensional practical classification problems where the number of attributes exceeds the number of instances. In this case, the researcher is faced with dual problems such as (i) biasedness towards the majority group over the minority group and (ii) dimensionality or singularity of the covariance matrix. In such situations, the classical classifiers dependent on the sample mean and covariance matrix are impracticable for classifications. This study focused on the effects of group imbalance data on $$n>p$$ classification problems. First, we develop a procedure that could transform the minority group into the majority group before the classifiers are applied. This study aims to determine whether there are observable effects on the classifiers’ performance for imbalanced and balanced data sets. We also investigated whether the over and under-sampling influence the computational time of the classifiers. The results revealed that the Fisher linear classification method (FLCM) performed comparably for imbalanced and balanced data and outperformed the nearest mean classifier (NMC) and the independent classification rule (ICR). The study demonstrated that the MVCT effects on the classifiers are data-dependent. Therefore, the investigation showed that sample size balancing irrespective of the data dimension does not have a strong impact on the classifier’s performance. This analysis concludes that for the $$n>p$$ classification problem, the FLCM classifier has comparable performance on the imbalanced and balanced data, similar results were observed for the NMC and ICR classifiers. The over and under-sampling of the data set has insignificant effects on the computational time of the classifiers.
Numerous studies show the ability of fetuses for affective evaluation and sensitivity to the sounds and rhythms of other human presence. This shows an appearance of fetuses’ perception in intentional engagement with the environment. It means that fetuses are able to select the relevant stimulus from the noisy environment with a cacophony of other stimuli: chemical interactions, pressure changes, and electromagnetic fields. This ability can appear in ecological learning only. The theoretical study observes the literature to understand what environmental features of the mother-fetus communication model enable a fetus to interact with the mother in ecological training. The objective is to design Human-Machine Systems and, specifically, computer-aided Medical Diagnosis systems based on the mother-fetus communication model. The article proposes the physiological mechanism of shared intentionality that relies on the mother's heart pulsed electromagnetic field (PEMF) impact on the adenosine receptors in both organisms. The study creates the concept design for future research to provide evidence of the mother-fetus communication model and establish human-computer connectivity.
This study investigates the relationship between news sentiment and the stock market's return. The sentiment was automatically analyzed using four methods, including lexicon-based and deep learning-based approaches, at three levels of granularity, i.e., sentence, paragraph, and full text. The sentiment was combined with features from the calendar year, lagged returns, and news publishers, which were fed into the XGBoost algorithm trained to classify the direction of market return for the following business day. The performance was maximized using Bayesian hyperparameter optimization and evaluated using nested cross-validation. The proof of concept was demonstrated using ten companies in the Dow Jones Index, which were grouped into five sectors. The findings indicate an asymmetric power of sentiment measures in different sectors, with the petroleum industry being the most responsive to the sentiment expressed in the news. The study highlights the significance of targeted sentiment measures in making informed decisions about the market direction, particularly for the petroleum industry.
The current technological advancements revolutionizing the concept of Urban Air Mobility (UAM), has a concurrent need to quantify the operational safety of these vehicles in terms of their associated risk. Providing safety certification of flight operations of UAM vehicles is critical as the concept relies on battery powered electrically Vertical Takeoff and Landing (eVTOL) vehicles, to operate in the current Air traffic control. In this paper, a data-driven method for UAM vehicle energy consumption prediction and risk quantification with conditional value-at-risk based on energy consumption distribution is presented. Significant factors affecting energy consumption, such as density altitude, aircraft design, airspeed, and collision avoidance algorithms, are considered in the data-driven based energy consumption prediction of multiple eVTOL flights. Additionally, a risk metric was deployed to evaluate the risk associated with worst case energy dissipating flights. Our result shows that the proposed approach provides a generalized method to quantify operational safety of UAM network over a given region.
