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Smart grid (SG) is an integration of traditional power grid with advanced information and communication infrastructure for bidirectional energy flow between grid and end users. A huge amount of data is being generated by various smart devices deployed in SG systems. Such a massive data generation from various smart devices in SG systems may generate issues such as-congestion, and available bandwidth on the networking infrastructure deployed between users and the grid. Hence, an efficient data transmission technique is required for providing desired QoS to the end users in this environment. Generally, the data generated by smart devices in SG has high dimensions in the form of multiple heterogeneous attributes, values of which are changed with time. The high dimensions of data may affect the performance of most of the designed solutions in this environment. Most of the existing schemes reported in the literature have complex operations for data dimensionality reduction problem which may deteriorate the performance of any implemented solution for this problem. To address these challenges, in this paper, a tensor-based big data management scheme is proposed for dimensionality reduction problem of big data generated from various smart devices. In the proposed scheme, firstly the Frobenius norm is applied on high-order tensors (used for data representation) to minimize the reconstruction error of the reduced tensors. Then, an empirical probability-based control algorithm is designed to estimate an optimal path to forward the reduced data using software-defined networks (SDN) for minimization of the load and effective bandwidth utilization on the network infrastructure. The proposed scheme minimizes the transmission delay occurred during the movement of the dimensionally reduced data between different nodes. The efficacy of the proposed scheme has been evaluated using extensive simulations carried out on the data traces using 'R' programming and Matlab. The big data traces considered for evaluation consist of more than 2 million entries (2075259) colecetd at 1 minute sampling rate having hetrogenous features such as-voltage, energy, frequency, electric signals, etc. Moreover, a comparative study for different data traces and a real SG testbed is also presented to prove the efficacy of the proposed scheme. The results obtained depict the effectiveness of the proposed scheme with respect to the parameters such as-network delay, accuracy, and throughput.

This study suggests that caregivers of patients with schizophrenia experience higher stigma than the caregivers of patients with bipolar disorder and recurrent depressive disorder. Higher stigma is associated with higher psychological morbidity in the caregivers. Therefore, the clinicians managing patients with severe mental disorders must focus on stigma and psychological distress among the caregivers and plan intervention strategies to reduce stigma.

In this paper, an explainable intelligence model that gives the logic behind the decisions unmanned aerial vehicle (UAV) makes when it is on a predefined mission and chooses to deviate from its designated path is developed. The explainable model is on a visual platform in the format of if-then rules derived from the Sugeno-type fuzzy inference model. The model is tested using the data recorded from three different missions. In each mission, adverse weather, conditions and enemy locations are introduced at random locations along the path of the mission. There are two phases to the model development. In the first phase, the Mamdani fuzzy model is used to create rules to steer the UAV along the designated mission and the rules of engagement when it encounters weather and enemy locations along and near its chosen mission. The data are gathered as UAV traverses on each mission. In the second phase, the data gathered from these missions are used to create a reverse model using a Sugeno-type fuzzy inference system based on the subtractive clustering in the data. The model has seven inputs (time, x-coordinate, y-coordinate, heading direction, engage in attack, continue mission, and steer UAV) and two outputs (weather conditions and distance from the enemy). This model predicts the outputs regarding the weather conditions and enemy positions whenever UAV deviates from the predefined path. The model is optimized with respect to the number of rules and prediction accuracy by adjusting subtractive clustering parameters. The model is then fine-tuned with ANFIS. The final model has six rules and root mean square error value that is less than 0.05. Furthermore, to check the robustness of the model, the Gaussian random noise is added to a UAV path, and the prediction accuracy is validated.INDEX TERMS Explainable artificial intelligence (XAI), fuzzy logic, ANFIS, unmanned aerial vehicle (UAV), subtractive clustering. I. INTRODUCTIONUnmanned Air Vehicles(UAVs) are used for many purposes including agriculture, industry, law enforcement, and defense. These autonomous systems have several advantages over manned aerial vehicles as not only they reduce expenses by avoiding human error, but they also save the lives of fighter jet pilots.The incoming generation of artificial intelligence(AI) systems are showing significant success through the use of various machine learning techniques. These systems offer a wide range of benefits when it comes to simplifying the lives of individuals as well as military operations. Continued advances promise to produce autonomous systems that will perceive, learn, decide, and act on their own. However, the effectiveness of today's AI systems is limited by the inability of the machine to explain its decisions and actions to human users [1]-[3]. This is where the concept of Explainable Artificial Intelligence (XAI) comes in to play. XAI aims to create a suite of machine learning techniques that will produce more explainable models while maintaining a high level of learning performance (prediction accuracy)....

The present study developed a novel approach to modeling indoor air quality (IAQ) of a public transportation bus by the development of hybrid genetic-algorithm-based neural networks (also known as evolutionary neural networks) with input variables optimized from using the regression trees, referred as the GART approach. This study validated the applicability of the GART modeling approach in solving complex nonlinear systems by accurately predicting the monitored contaminants of carbon dioxide (CO 2 ), carbon monoxide (CO), nitric oxide (NO), sulfur dioxide (SO 2 ), 0.3-0.4 µm sized particle numbers, 0.4-0.5 µm sized particle numbers, particulate matter (PM) concentrations less than 1.0 µm (PM 1.0 ), and PM concentrations less than 2.5 µm (PM 2.5 ) inside a public transportation bus operating on 20% grade biodiesel in Toledo, OH.First, the important variables affecting each monitored in-bus contaminant were determined using regression trees. Second, the analysis of variance was used as a complimentary sensitivity analysis to the regression tree results to determine a subset of statistically significant variables affecting each monitored in-bus contaminant. Finally, the identified subsets of statistically significant variables were used as inputs to develop three artificial neural network (ANN) models. The models developed were regression tree-based backpropagation network (BPN-RT), regression tree-based radial basis function network (RBFN-RT), and GART models. Performance measures were used to validate the predictive capacity of the developed IAQ models. The results from this approach were compared with the results obtained from using a theoretical approach and a generalized practicable approach to modeling IAQ that included the consideration of additional independent variables when developing the aforementioned ANN models. The hybrid GART models were able to capture majority of the variance in the monitored in-bus contaminants. The genetic-algorithm-based neural network IAQ models outperformed the traditional ANN methods of the back-propagation and the radial basis function networks.Implications: The novelty of this research is the development of a novel approach to modeling vehicular indoor air quality by integration of the advanced methods of genetic algorithms, regression trees, and the analysis of variance for the monitored in-vehicle gaseous and particulate matter contaminants, and comparing the results obtained from using the developed approach with conventional artificial intelligence techniques of back propagation networks and radial basis function networks. This study validated the newly developed approach using holdout and threefold cross-validation methods. These results are of great interest to scientists, researchers, and the public in understanding the various aspects of modeling an indoor microenvironment. This methodology can easily be extended to other fields of study also.

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