Insurance risk prediction is very difficult, due to the diversity and richness of customer information. The traditional statistical or independent deep learning models often face the challenge of trading off accuracy, interpretability, and deployment efficiency. We introduce a Hybrid DNN–DevOps Framework, as it integrates use of CNN, GRU and Attention to more efficiently capture the spatial-temporal relationships in claim data and manage for ongoing optimization using DevOps automation. With advanced features like containerized deployment, ML flow tracking, and Prometheus-based monitoring we can offer real-time reliability. Extensive experiments demonstrate the efficiency and effectiveness of proposed model with 98.4% accuracy, 98.15% F1-score for speech VR, and only 48 ms latency comparing with state-of-art methods such as Random Forest(94.8%) and CNN–LSTM (96.8%). The results verify the scalability of the framework, its stability to data drift and the ability to run efficiently in dynamic cloud environments. The combination of automated retraining pipelines, and predictive intelligence, enhance claim analysis, fraud discovery and policy optimisation making the proposed system competitive for advanced insurance analytics. The findings provide a solid basis for intelligent, explainable and scalable risk prediction systems in banking and insurance sectors.
This research discusses the combination of the “federated learning and blockchain technology to optimize fraud detection” in the cross-border transactions. With the increase in digital payment systems across the world, the need for safe and privacy-involving solutions rises to the fore. The federal learning does not share the sensitive data but is able to train the machine learning models in multiple organizations at the same time which is a novel approach incorporating in fraud detection by the nature of blockchain which is decentralized and immutable. Four Machine Learning algorithms, namely, XGBoost, CatBoost, Random Forest and Logistic Regression were attempted and assessed in terms of their performance in a Federated Learning scenario. “The results of the experiment showed that the federated learning was superior to the traditional learning, which demonstrated the accuracy of 94.7
The semiconductor industry has been persistently grappling with the challenges posed by complex and data- centric supply chains, suffering from issues like demand uncertainty, high production variability and lack of real time visibility across globally dispersed manufacturing nodes. Conventional systems often do not capture active process dependencies, leading to inefficiency and indecisiveness. In response to these limitations, this research proposes an AI- Driven Data Engineering and Predictive Analytics Framework designed for smarter semiconductor supply chain optimization and digital infrastructure transformation. It is designed to utilize novel data engineering pipelines coupled with a Hybrid Random Forest-LSTM approach for failing prediction and process anomaly detection through the SECOM (UCI) dataset and Variability in Semiconductor Manufacturing datasets. Random Forest clarity in feature interpretation and dimensionality reduction with LSTM captures temporal correlations between process stages. The proposed system achieved remarkable performance in terms of accuracy, prediction errors and yield forecasting. This hybrid method offers a greater level of simplification, adaptability in real time to changing conditions and operational flexibility over traditional numerical models or single-model approaches. The outcomes show measurable improvements in manufacturing flow, reducing supply-chain disruptions and leading sustainable semiconductor fabrication through smart automation and data- informed outcome leverage.
In order to make modern supply chains more profitable and environmentally sustainable, logistics routing and transportation optimisation are crucial. Efficient transportation and routing strategies are crucial in logistics operations because they cover the whole economic lifecycle, from sourcing raw materials to ultimate delivery. Improved data quality was achieved in this study by the use of data preparation techniques like feature engineering, encoding categorical variables, addressing missing values, and transformation. Hierarchical clustering and K-means were two of the clustering approaches used for comparative analysis in order to classify logistics providers. In addition, Differential Evolution (DE), GA, Simulated Annealing (SA), and Prism Refraction Search (PRS) were employed as optimisation methods to enhance transportation and logistics routing. To strike a better balance between global and local search capabilities, a new hybrid approach called BiPRS-SA was created by combining the strengths of these algorithms. A high accuracy of 97.19% was attained using an ensemble modelling technique, suggesting steady and robust prediction performance, and the results show that the suggested hybrid method greatly enhances optimisation efficiency. The combination of ensemble methodologies with modern optimisation algorithms improves transportation and logistics optimisation decision-making, which in turn leads to higher sustainability, lower costs, and operational efficiency.
