A Survey of Emerging Technologies in Cloud Enterprise Resource Planning: The Oracle Fusion Cloud ERP Ecosystem

Vamsi Thatikonda — 2026-08-17

Enterprise Resource Planning (ERP) systems have evolved from monolithic, on-premises transaction engines into cloud-native, intelligent platforms that fuse artificial intelligence, distributed ledgers, the Internet of Things (IoT), and elastic edge-to-cloud infrastructure. Oracle Fusion Cloud ERP has emerged as a leading exemplar of this transformation, embedding machine learning, generative and agentic artificial intelligence, and pervasive analytics directly within core financial and operational workflows. This article presents a structured survey of the emerging technologies reshaping the Oracle ERP ecosystem and situates them within the broader research landscape of intelligent enterprise systems. The survey traces the architectural evolution from Oracle E-Business Suite to Fusion Cloud ERP; examines the cloud and edge computing foundations provided by Oracle Cloud Infrastructure and the Oracle Autonomous Database; analyzes embedded, generative, and agentic AI capabilities, including the AI Agent Studio and a suite of finance-oriented agents; and reviews the integration of IoT and sensor networks, blockchain-based track-and-trace, data science and soft-computing techniques, and cybersecurity mechanisms. Recurring design patterns and open research challenges are synthesized across trust, explainability, data governance, latency-sensitive edge processing, and the security of autonomous enterprise agents. The result is a technology map intended to serve both practitioners planning cloud ERP modernization and researchers seeking an application-grounded view of one of the most consequential domains for AI, IoT, blockchain, and secure distributed computing.

Deep Learning-based Early Prediction of Heart Disease using Clinical Healthcare Data

Bipin Sule — 2026-08-10

Cardiovascular Diseases (CVD) are the number one cause of death globally and are responsible for a significant proportion of all global deaths annually, and are particularly prevalent in LMICs where diagnostic resources are scarce. Accurate and early prediction of heart disease risk from routinely collected clinical information can enable early intervention, minimize diagnostic delay, and reduce long-term treatment. This article presents a hybrid deep learning model that processes one-dimensional Convolutional Neural Networks (CNNs) for local feature extraction, a Long Short-Term Memory (LSTM) network for temporal and sequential modeling of the longitudinal clinical measurements, and a fully connected classification head with an attention mechanism to attend to clinically salient features. The model is trained and tested using a combined dataset consisting of the Cleveland, Hungarian, Switzerland, and Long Beach VA subsets of the UCI Heart Disease repository, along with the Framingham and Z-Alizadeh Sani datasets to enhance the model's generalizability. The missing values are handled using multiple imputation; the Synthetic Minority Oversampling Technique (SMOTE) is used to combat class imbalance, and z-score normalization is applied to scale the features. Experimental results are then shown, which includes an accuracy of 94.6%, sensitivity of 93.8%, specificity of 95.1%, F1-score of 94.2% and an Area Under the Receiver Operating Characteristic Curve (AUC-ROC) of 0.978, outperforming the deep learning baselines (plain CNN, plain LSTM, deep neural network) and classical machine learning models (logistic regression, random forest, support vector machine, XGBoost) when tested under the same conditions. Using SHAP (SHapley Additive exPlanations) model interpretation, the most influential parameters are chest pain type, maximum heart rate achieved, number of major vessels colored by fluoroscopy, and thalassemia status, in accordance with known clinical knowledge. The outcomes indicate that the proposed framework can be a promising decision support tool in primary care and resource-constrained environments, and that it is interpretable and computationally efficient.

International Journal of Mobile and Cloud Systems Engineering (e-ISSN: 3108-3315)

A Survey of Emerging Technologies in Cloud Enterprise Resource Planning: The Oracle Fusion Cloud ERP Ecosystem

Deep Learning-based Early Prediction of Heart Disease using Clinical Healthcare Data