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Globally, traffic accidents are of main concern because of more death rates and economic losses every year. Thus, road accident severity is the most important issue of concern, mainly in the undeveloped countries. Generally, traffic accidents result in severe human fatalities and large economic losses in real-world circumstances. Moreover, appropriate, precise prediction of traffic accidents has a high probability with regard to safeguarding public security as well as decreasing economic losses. Hence, the conventional accident prediction techniques are usually devised with statistical evaluations, which identify and evaluate the fundamental relationships among human variability, environmental aspects, traffic accidents and road geometry. However, the conventional approaches have major restrictions based on the assumptions regarding function kind and data distribution. In this paper, Aquila Anti-Coronavirus Optimization-based Deep Long Short-Term Memory (AACO-based Deep LSTM) is developed for road accident severity detection. Spearman’s rank correlation coefficient and Deep Recurrent Neural Network (DRNN) are utilized for the feature fusion process. Data augmentation method is carried out to improve the detection performance. Deep LSTM detects the road accident and its severity, where Deep LSTM is trained by the designed AACO algorithm for better performance. The developed AACO-based Deep LSTM model outperformed other existing methods with the Mean Square Error (MSE), Root-Mean-Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) of 0.0145, 0.1204 and 0.075%, respectively.

The huge response time taken by the analytical queries is one of the most challenging problems in a data warehouse. The main reason for this high response time is the enormous amount of data being queried and the complex nature of the queries. This problem can be addressed with Materialized View Selection (MVS), where the optimal views with low response time are selected. To this end, the possible views for the analytical queries are randomly defined in the search space. Since most of the existing solutions are formulated based on semi-optimal solutions, this work introduces a hybrid metaheuristic-based framework to address the issue reliably. The major gaps in the existing research works are the resource wastage issue and the space constraint issue. These gaps are aimed to be addressed by the proposed hybrid framework. The proposed work integrates a Genetic Algorithm (GA) with Aquila Optimizer (AO) to search for the most optimal views from the search space. The presented solution follows highly efficient exploratory and exploitation behaviors to optimize the considered problem. Based on an iterative procedure, the most optimal views are selected by the hybrid framework, and these views are evaluated for minimum cost and time. Finally, performance evaluations are carried out to signify the practicality of the proposed approach.

Aquila optimizer (AO) is an efficient meta-heuristic optimization method, which mimics the hunting style of Aquila in nature. However, the AO algorithm may suffer from immature convergence during the exploitation stage. In this paper, two strategies are elegantly employed into conventional AO, such as random opposition-based learning and nonlinear flexible jumping factor, which can efficiently enhance the performance of conventional AO. Experiments on $17$ benchmark functions and image segmentation demonstrate the effectiveness of the proposed algorithm. Comparison with several state-of-the-art meta-heuristic optimization techniques indicates the efficacy of the developed method.

In recent years, rainfall prediction has received major attention in research areas because of its demanding applications in pollution control management and flood control management. Despite having numerous learning-based approaches to calculate future rainfall trends, it remains inefficient to predict rainfall occurrences by learning linear and nonlinear data patterns of historical weather information (i.e., exact prediction value is complicated to be predicted). These complications are addressed with the evolution of stochastic models which have a greater ability to minimize prediction bias and represent long-term weather variability. Therefore, this paper proposes a novel modified stochastic fuzzy Aquila (MSFA) algorithm to make precise predictions regarding future trends by evaluating rainfall time series data. The proposed MSFA algorithm is applied in rainfall prediction applications in evaluating the effectiveness of the proposed stochastic model. Here, 10 features of the open weather dataset collected from Tamil Nadu are provided as input for the proposed rainfall prediction design. The data inconsistencies such as undesirable format and missing values are structured using preprocessing procedures, namely data arrangement, null value removal, and data partitioning. The preprocessed data are fed into the proposed MSFA algorithm which learns the data features more precisely and predicts the probable occurrence of rainfall. To evaluate the performances of the proposed MSFA algorithm, the metrics such as mean absolute error (MAE), coefficient of determination, root mean squared logarithmic error (RMSLE), and root mean square error (RMSE) are analyzed. The experimental results illustrate that the proposed MSFA algorithm achieves superior performance in terms of all metrics.