Acoustic data analysis has emerged as a critical area of exploration for the detection of different events for quick actions in smart traffic management systems, particularly in traffic management and safety for a step toward smart cities. A specific challenge is precisely classifying road noises and emergency vehicles using sound, which is essential for speeding up emergency response times and improving traffic flow management. While existing solutions address this problem, there is opportunity for enhancement in terms of precision and accuracy to enhance the traffic flow in a sustainable smart city through a demanding and innovative technique. In this study, we suggest stacking ensemble deep learning techniques to intelligently classify emergency vehicle sirens from various background noises using a data set of traffic collected on roads via microphone sensors. The ensemble model incorporates Multi-Layer Perceptron (MLP) and Deep Neural Network (DNN) as base-learners, with an LSTM model as a metalearner. This approach not only optimizes model efficiency but also facilitates advanced feature engineering to extract useful features including Mel Frequency Cepstral Coefficients (MFCC), Z-score, root mean square (RMS), spectral centroids, spectral flux, mel spectrogram, chroma, contrast, and Tonnetz. Using these features, our proposed Stacking Ensemble LSTM successfully classified traffic noises and emergency vehicle sirens with the highest efficiency. Upon evaluation of the test set of data for our proposed model, it has gained an accuracy of 99.12% with F1 scores ranging from 98%. This significant improvement highlights the dominance of our proposed model approach over prior research. Our proposed model presents assurance in advancing traffic control and safety statutes, demonstrating potential applicability in daily intelligent transportation systems.

In this study, we look at how to estimate stress–strength reliability models, R₁ = P (Y < X) and R₂ = P (Y < X), where the strength X and stress Y have the same distribution in the first model, R₁, and strength X and stress Z have different distributions in the second model, R₂. Based on the first model, the stress Y and strength X are assumed to have the Lomax distributions, whereas, in the second model, X and Z are assumed to have both the Lomax and inverse Lomax distributions, respectively. With the assumption that the variables in both models are independent, the median-ranked set sampling (MRSS) strategy is used to look at different possibilities. Using the maximum likelihood technique and an MRSS design, we derive the reliability estimators for both models when the strength and stress variables have a similar or dissimilar set size. The simulation study is used to verify the accuracy of various estimates. In most cases, the simulation results show that the reliability estimates for the second model are more efficient than those for the first model in the case of dissimilar set sizes. However, with identical set sizes, the reliability estimates for the first model are more efficient than the equivalent estimates for the second model. Medical data are used for further illustration, allowing the theoretical conclusions to be verified.

Genetic algorithms (GAs) are stochastic-based heuristic search techniques that incorporate three primary operators: selection, crossover, and mutation. These operators are supportive in obtaining the optimal solution for constrained optimization problems. Each operator has its own benefits, but selection of chromosomes is one of the most essential operators for optimal performance of the algorithms. In this paper, an improved genetic algorithm-based novel selection scheme, i.e., stairwise selection (SWS) is presented to handle the problems of exploration (population diversity) and exploitation (selection pressure). For its global performance, we compared with several other selection schemes by using ten well-known benchmark functions under various dimensions. For a close comparison, we also examined the significance of SWS based on the statistical results. Chi-square goodness of fit test is also used to evaluate the overall performance of the selection process, i.e., mean difference between observed and expected number of offspring. Hence, the overall empirical results along with graphical representation endorse that the SWS outperformed in terms of robustness, stability, and effectiveness other competitors through authentication of performance index (PI).

Median ranked set sampling is a sampling procedure used to estimate the population mean when the variable of interest is difficult or costly to measure. Two estimators for the population mean based on the minimum and maximum values of the auxiliary variable are built upon a successive use of ranks, second raw moments, and the linearly transformed auxiliary variable. The biases and the mean square errors of the estimators are derived. The proposed estimators under median ranked set sampling have higher efficiencies than the ratio, regression, difference-cum-ratio, and exponential estimators.

Tourists play a vital role in the development of sustainable tourism by boosting the local economy, preserving culture, and reducing environmental degradation. However, tourists’ decisions to visit destinations and promote sustainable tourism are influenced by reliable infrastructure, safety, security, and trust in institutions. A well-developed infrastructure, a peaceful environment, and efficient tourism management encourage tourism and sustainability. Therefore, this research examines the effect of tourism, infrastructure, institutions, and place attachment on sustainable tourism. Additionally, the study examines the role of infrastructure, institutions, and place attachment on tourists’ satisfaction in Khyber Pakhtunkhwa. Primary data were collected through surveys and Google forms from five tourist districts, Swat, Abbottabad, Mansehra, Upper Dir, and Lower Dir of Khyber Pakhtunkhwa. A total of 250 responses were collected from tourists using a proportionate random sampling technique. The data were analyzed using the partial least square structural equation modeling (PLS-SEM) technique. The results show that place attachment and tourist satisfaction positively affect sustainable tourism, while infrastructure, place attachment, and trust in institutions positively affect tourist satisfaction. Furthermore, sustainable tourism was more influenced by tourist satisfaction, and tourist satisfaction was more influenced by trust in institutions and infrastructure. The study also confirmed that infrastructure mediates the relationship between trust in institutions and sustainable tourism and between trust in institutions and tourist satisfaction. The study provides valuable insight to policymakers in assessing tourists’ behavior and sustainable tourism.

The appealing traits of carbon nanotubes (CNTs) encompassing mechanical and chemical steadiness, exceptional electrical and thermal conductivities, lightweight, and physiochemical reliability make them desired materials in engineering gadgets. Considering such stimulating characteristics of carbon nanotubes, our goal in the current study is to scrutinize the comparative analysis of Darcy-Forchheimer nanofluid flows containing CNTs of both types of multi and single-wall carbon nanotubes (MWCNTs, SWCNTs) immersed into two different base fluids over a stretched surface. The originality of the model being presented is the implementation of the induced magnetic field that triggers the electric conductivity of carbon nanotubes. Moreover, the envisioned model is also analyzed with homogeneous-heterogeneous (h-h) chemical reactions and heat source/sink. The second-order slip constraint is assumed at the boundary of the surface. The transmuted high-nonlinearity ordinary differential equations (ODEs) are attained from the governing set of equations via similarity transformations. The bvp4c scheme is engaged to get the numerical results. The influence of different parameters is depicted via graphs. For both CNTs, the rate of heat flux and the surface drag coefficient are calculated using tables. It is highlighted that an increase in liquid velocity is witnessed for a varied counts volume fraction of nanoparticles. Also, Single-wall water-based carbon nanotube fluid has comparatively stronger effects on concentration than the multi-walled carbon nanotubes in water-based liquid. The analysis also indicates that the rate of heat flux and the surface drag coefficient are augmented for both SWCNTs and MWCNTs for different physical parameters. The said model is also validated by comparing it with a published result.