In cloud computing, task scheduling has become more and more important for maximizing resource allocation, cutting down on energy use, and enhancing system performance. However, traditional algorithms often fall short in dynamic environments, leading to performance inefficiencies. For cloud computing energy efficiency, this research suggests a hybrid task scheduling method that combines Transformer-based fine-tuning and Cuckoo Search (HCS) optimization. The integration of bio-inspired optimization (HCS) and Modified BERT-Based Transformer allows for dynamic task reallocation, enhancing both energy consumption and resource utilization while maintaining high Service Level Agreement (SLA) compliance. Fine-tuning with Transformer models enables adjustments to improve scheduling efficiency and minimize energy wastage. The results show that the proposed method reduced energy consumption to 0.430 J (from 0.440 J) while maintaining 100% SLA compliance and task completion rate. Resource utilization improved to 88.2%, with energy efficiency of 0.0043 J/task. This hybrid approach offers significant advantages over traditional methods, such as BENBO + Bi-LSTM (0.687 J), PSO-PGA (0.617 J), and GAECS (0.537 J), achieving a reduced energy consumption of 0.440 J for 15 PM with 20 VMs, making it an ideal solution for scalable cloud computing environments.

Vermicomposting is one of the most important waste management techniques in the process of vermiculture. In this study, a neural network-assisted novel paradigm is proposed to predict waste from vermicomposting. The proposed neural network skeleton is based on a gallium arsenide processing schema, which is used to separate wastes. By comparing the proposed system with existing methods, it was found that the proposed approach had the highest average prediction ratio of 91.32%, outperforming other techniques like the encoder-recurrent decoder (ERD) network, recurrent neural network (RNN), and deep long short-term memory (deep LSTM) network. The separation ratio analysis also demonstrated the effectiveness of the proposed method, with a range of 45–94%. Furthermore, the study emphasizes the importance of chemical equilibrium and the effectiveness of our proposed gallium arsenide processing schema in achieving high prediction and separation accuracies, showcasing its potential for practical application in waste management processes. Lastly, the prediction of the process evolution stages is detailed, indicating the efficiency of the proposed system in achieving various levels of waste separation. Overall, the study provides valuable insights into the potential of the proposed methods in optimizing wastewater management processes, paving the way for more effective and sustainable vermicomposting practices.

Vermicomposting is one of the most important waste management techniques in the process of vermiculture. In this study, a Neural Network-assisted novel paradigm is proposed to predict waste from vermicomposting. The proposed Neural Network Skeleton is based on a Gallium Arsenide Processing Schema, which is used to separate wastes. By comparing the proposed sys-tem with existing methods, it was found that the proposed approach had the highest average prediction ratio of 91.32%, outperforming other techniques like ERD, RNN, and Deep LSTM. The separation ratio analysis also demonstrated the effectiveness of the proposed method, with a range of 45-94%. Furthermore, the study emphasizes the importance of chemical equilibrium and the effectiveness of our proposed Gallium Arsenide Processing Schema in achieving high prediction and separation accuracies, showcasing its potential for practical application in waste management processes. Lastly, the prediction of the process evolution stages is detailed, indicating the efficiency of the proposed system in achieving various levels of waste separation. Overall, the study provides valuable insights into the potential of the proposed methods in op-timizing wastewater management processes, paving the way for more effective and sustainable vermicomposting practices.