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An abundance of techniques has been presented so forth for waste classification but, they deliver inefficient results with low accuracy. Their achievement on various repositories is different and also, there is insufficiency of high-scale databases for training. The purpose of the study is to provide high security.

In this research, optimization-assisted federated learning (FL) is introduced for thermoplastic waste segregation and classification. The deep learning (DL) network trained by Archimedes Henry gas solubility optimization (AHGSO) is used for the classification of plastic and resin types. The deep quantum neural networks (DQNN) is used for first-level classification and the deep max-out network (DMN) is employed for second-level classification. This developed AHGSO is obtained by blending the features of Archimedes optimization algorithm (AOA) and Henry gas solubility optimization (HGSO). The entities included in this approach are nodes and servers. Local training is carried out depending on local data and updations to the server are performed. Then, the model is aggregated at the server. Thereafter, each node downloads the global model and the update training is executed depending on the downloaded global and the local model till it achieves the satisfied condition. Finally, local update and aggregation at the server is altered based on the average method. The Data tag suite (DATS_2022) dataset is used for multilevel thermoplastic waste segregation and classification.

By using the DQNN in first-level classification the designed optimization-assisted FL has gained an accuracy of 0.930, mean average precision (MAP) of 0.933, false positive rate (FPR) of 0.213, loss function of 0.211, mean square error (MSE) of 0.328 and root mean square error (RMSE) of 0.572. In the second level classification, by using DMN the accuracy, MAP, FPR, loss function, MSE and RMSE are 0.932, 0.935, 0.093, 0.068, 0.303 and 0.551.

The multilevel thermoplastic waste segregation and classification using the proposed model is accurate and improves the effectiveness of the classification.

This paper aims to optimize the mass of a tethered aerostat to achieve optimum hull volume, and fins to generate aerodynamic lift to reduce the blow-by.

The design code of aerostat involving structure, aerostatics, aerodynamics and stability has been developed using MATLAB®. The design code is used to obtain the baseline configuration for a tactical aerostat mission by using the statistical values of the hull fineness ratio and the fin parameters of in-service aerostats. The effect of the design variables that include the hull fineness ratio, fin area and fin position on the aerostat mass and blow-by is determined through sensitivity analysis. The aerostat is optimized with an objective function of minimization of mass for the bounded values of design variables and taking blow-by limit as a constraint.

This study reveals that the simultaneous optimization of the aerostat hull fineness ratio, fin area and fin position results in an improvement in the design. The aerostat design with optimum values of these parameters helps in a reduction in its size and mass without compromising the blow-by limits.

This study has been conducted by keeping the hull shape constant by selecting standard National Physics Laboratory envelope shape. The aerodynamic model used in the design code is based on empirical relationships that can be improved in future studies that can use high fidelity aerodynamic models using CFD based surrogate models.

The previous studies on optimization of aerostats are limited to hull envelope shape only, whereas this paper presents the optimization of the hull and fin together. The optimized configuration obtained has a reduced mass and can operate within the specified blow-by limits.