The increasing reliance on cloud storage across sectors has made the protection of sensitive data a critical concern especially as advances in quantum computing threaten to break traditional public-key cryptographic systems. This growing vulnerability raises urgent questions about how to secure digital assets in a post-quantum future. To address this challenge, this study aims to design, implement, and evaluate a hybrid encryption framework that combines symmetric encryption for data confidentiality with quantum-resistant public-key algorithms for key management and digital signatures. The objective was to develop a secure and practical solution that can be readily adopted using open-source tools. The system was built in a Linux-based virtual environment using an S3-compatible object storage platform, integrated with quantum-safe cryptographic libraries, and automated through scripting. Performance testing was conducted using files ranging from 100 MB to 1 GB, with metrics such as encryption and decryption time, CPU usage, upload/download latency, and internet throughput analyzed. The results showed that while quantum-safe operations added some overhead, even large files could be securely processed in under two minutes with stable system performance and no data corruption. These findings confirm that hybrid encryption models can be deployed today without specialized hardware, offering a forward-compatible and robust approach to cloud security. This work contributes a tested and reproducible framework for early adoption of quantum-safe cryptography in real-world cloud environments.

This research dives into temperature prediction, focusing on its practical applications and how advanced Machine Learning techniques can improve accuracy. It specifically examines the use of long short-term memory (LSTM) networks in enhancing forecasting capabilities. The study uses data from Delhi, India, to train and assess the LSTM model. This paper finds key gaps in temperature forecasting research and explores their impact on industries and daily life. Accurate temperature prediction are important for sectors such as agriculture, transportation, and disaster management, where even small improvements can have significant effects. The research provides a detailed examination of the LSTM model, including its structure, validation strategy, and evaluation process. LSTM networks are ideal for temperature forecasting because they can effectively capture long-term patterns in time series data. The study demonstrates the effectiveness of LSTM networks in improving temperature prediction accuracy and discusses their potential to transform industries relying on accurate forecasts. This research trained a model with an efficient evaluation of MSE 0.034.