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Abstract Researchers are now avoiding perovskite materials containing lead due to their toxicity and instability in open air and heat. In this study, our research group worked on potassium-based K2MgSiCl6 & K2MgSiBr6 perovskite compounds and thoroughly investigated these materials’ basic behaviours. Goldschmidt and formation energy results certify the structure and thermodynamic stabilities of the studied compounds. The band gaps noted for the K2MgSiCl6 & K2MgSiBr6 perovskites were 2.42 eV & 1.88 eV, respectively. After the mechanical observations of the investigated compounds, it became apparent that both the studied compounds own brittle and anisotropic nature. Based on the tendency to absorb electromagnetic radiations both in the ultra-violet and visible range, it enables the K2MgSiCl6 & K2MgSiBr6 perovskites suitable for detectors, solar cells, and many other optoelectronic devices. The K2MgSiBr6 material showed the first response to the electromagnetic radiations in the visible range and acquired the optical conductivity value of 1219 (Ω.cm)-1; however, K2MgSiCl6 material showed its response to a bit higher energetic photons in the visible range than K2MgSiBr6 material, but its optical conductivity was higher (i.e., 1270 (Ω.cm)-1) than the K2MgSiBr6 material in the visible range.

Sonar sound datasets are of significant importance in the domains of underwater surveillance and marine research as they enable experts to discern intricate patterns within the depths of the water. Nevertheless, the task of classifying sonar sound datasets continues to pose significant challenges. In this study, we present a novel approach aimed at enhancing the precision and efficacy of sonar sound dataset classification. The integration of deep long-short-term memory (DLSTM) and convolutional neural networks (CNNs) models is employed in order to capitalize on their respective advantages while also utilizing distinctive feature engineering techniques to achieve the most favorable outcomes. Although DLSTM networks have demonstrated effectiveness in tasks involving sequence classification, attaining their optimal performance necessitates careful adjustment of hyperparameters. While traditional methods such as grid and random search are effective, they frequently encounter challenges related to computational inefficiencies. This study aims to investigate the unexplored capabilities of the fuzzy slime mould optimizer (FUZ-SMO) in the context of LSTM hyperparameter tuning, with the objective of addressing the existing research gap in this area. Drawing inspiration from the adaptive behavior exhibited by slime moulds, the FUZ-SMO proposes a novel approach to optimization. The amalgamated model, which combines CNN, LSTM, fuzzy, and SMO, exhibits a notable improvement in classification accuracy, outperforming conventional LSTM architectures by a margin of 2.142%. This model not only demonstrates accelerated convergence milestones but also displays significant resilience against overfitting tendencies.

Pulse repetition interval modulation (PRIM) is integral to radar identification in modern electronic support measure (ESM) and electronic intelligence (ELINT) systems. Various distortions, including missing pulses, spurious pulses, unintended jitters, and noise from radar antenna scans, often hinder the accurate recognition of PRIM. This research introduces a novel three-stage approach for PRIM recognition, emphasizing the innovative use of PRI sound. A transfer learning-aided deep convolutional neural network (DCNN) is initially used for feature extraction. This is followed by an extreme learning machine (ELM) for real-time PRIM classification. Finally, a gray wolf optimizer (GWO) refines the network's robustness. To evaluate the proposed method, we develop a real experimental dataset consisting of sound of six common PRI patterns. We utilized eight pre-trained DCNN architectures for evaluation, with VGG16 and ResNet50V2 notably achieving recognition accuracies of 97.53% and 96.92%. Integrating ELM and GWO further optimized the accuracy rates to 98.80% and 97.58. This research advances radar identification by offering an enhanced method for PRIM recognition, emphasizing the potential of PRI sound to address real-world distortions in ESM and ELINT systems.