Hybrid CNN-LSTM Powered Cloud Ecosystems: Advancing Finance, Healthcare, and Retail Through Unified AI Architectures

Abstract

The rapid digitalization of business processes in sectors such as finance, healthcare, and retail has led to a large amount of complicated data that needs robust and intelligent processing mechanisms. This paper proposes a homogeneous AI-based cloud environment on the basis of a hybrid CNN and LSTM model in order to deal with prediction and anomaly detection problems. The methodology begins with the integration of heterogenous data sets pulled from public repositories, and over significant sectoral spaces. The data sets include structured data like patient data, financial data, and transaction streams as well as unstructured formats like text input and sensor streams. For model readiness and consensus, Min-Max Scaling is employed for data normalization, and dimensionality reduction is done using Principal Component Analysis. Feature engineering extracts useful patterns, usage data, and performance metrics. The core component of the model employs CNN for spatial feature learning and LSTM for modeling temporal dependency, which is well-suited to time-series and sequential forecasting. To achieve secure and verifiable data integrity, a blockchain protocol is implemented within the multi-cloud setup. Additionally, multi-view K-means clustering enhances classification accuracy by learning data from different perspectives. Execution is carried out using Python with TensorFlow and Scikit-learn packages to ensure flexibility and reproducibility. Results of experiments demonstrate that the proposed hybrid model achieves higher performance than traditional CNN and LSTM models when it comes to prediction accuracy, anomaly detection, and processing time. The system achieved a prediction accuracy of 92%, proving its effectiveness in intelligent cloud applications in different sectors.