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The soil quality plays a crucial role in providing essential nutrients for crop growth and ensuring a bountiful yield. Identifying the soil composition, which includes sand, silt particles, and the mixture of clay in specific proportions, is vital for making informed decisions about crop selection and managing weed growth. Furthermore, soil pollution from emerging contaminants presents a substantial risk to water resource management and food production. Developing numerical models to comprehensively describe the transport and reactions of chemicals within both the plants and soil is of utmost importance in crafting effective mitigation strategies. To address the limitations of traditional models, this paper devises an innovative approach that leverages deep learning to predict hydroponic and soil compound dynamics during plant growth. This method not only enhances the understanding of how plants interact with their environment but also aids in making more informed decisions about agriculture, ultimately contributing to more sustainable and efficient crop production. The data needed to perform the developed hydroponic and soil compound prediction model is acquired from online resources. After that, this data is forwarded to the feature extraction phase. The weighted features, deep belief network (DBN) features, and the original features are achieved in the feature extraction stage. To get the weighted features, the weights are optimally obtained using the Iteration-assisted Enhanced Mother Optimization Algorithm (IEMOA). Subsequently, these extracted features are fed into the Multi-Scale feature fusion-based Convolution Autoencoder with a Gated Recurrent Unit (MS-CAGRU) network for hydroponic and soil compound prediction. Thus, the hydroponic and soil compound prediction data is attained in the end. Finally, the performance evaluation of the suggested work is conducted and contrasted with numerous conventional models to showcase the system’s efficacy. [ABSTRACT FROM AUTHOR]

Ambient assisted living (AAL) is a groundbreaking approach that harnesses the power of smart technology to offer all-encompassing care and support for elderly and differently abled individuals in their day-to-day lives. Progressive innovation in AAL solutions can facilitate and support day-to-day routines, expanding the time they can live autonomously and supporting proficiency. This research mainly analyzes AAL's significant role in tending to the exceptional difficulties these populations face. AAL frameworks incorporate an array of sensors, gadgets, and intelligent calculations that help monitor current circumstances and exercises, empowering early recognition of peculiarities, fall counteraction, and customized help. This research introduces a novel attention transfer learning-based crossover chimp (ATL-CC) algorithm for AAL, which combines crossover-based chimp optimization with a transformer-based model for transfer learning, integrating an attention mechanism. The ATL-CC algorithm aims to enhance activity recognition and classification within AAL environments. Precision, accuracy, recall, root mean square error, and F1-score are evaluated, where accuracy attains the value of 98.9%, precision attains the value of 97.4%, recall attains the value of 98%, and F1-score attains the value of 96%. Overall, AAL arises as a promising arrangement that upholds the deprived and advances respect, independence, and inclusivity in maturing and various societies. [ABSTRACT FROM AUTHOR]