Design of an Improved Model for Multimodal Data Fusion Using XGBoost-LSTM-CNN and Proximal Policy Optimizations

Applications in healthcare, finance, and real-time sensor applications call for much stronger demands of improving accuracy and efficiency in the analysis of multimodal data. Current methods have deficiencies in fusing multiple types of data such as time-series, spatial, and categorical data, arising due to limitations in capturing sequential dependencies, spatial patterns, and feature importance simultaneously. It also discusses these challenges through a novel solution based on a hybrid ML-DL approach, with an integration of reinforcement learning and advanced probabilistic models. First, the method that comes to mind is the XGBoost-LSTM-CNN Hybrid Model, wherein three drivers of performance improvements come together, namely, XGBoost, with much-appreciated capability for outstanding handling of structured tabular data, LSTM due to its proficiency in capturing long-term temporal dependencies, and the strength of CNN in spatial feature extraction. These results improve the predictive accuracy for multimodal datasets. After that, further enhanced multimodal fusion by the CAMT selectively pays attention to the critical features across the modalities, enhancing the accuracy of the contextual time-series predictions. Subsequently, PPO enables real-time model adaptation by dynamic optimization of model parameters and improvement of predictions through continuous learning. KF-BNN reduces noise and uncertainty in time-series data by fusing the filtering capability of a Kalman filter with probabilistic modeling via Bayesian neural networks to give reliable predictions. It also provides federated learning via FedAvg with online gradient descent for distributed model training in a manner that ensures privacy, continuous model updates without having to centralize sensitive data samples. These approaches show significant improvements along many axes, with accuracy improvements of up to 4.5% and MAE reductions of 12% relative to the baseline models. Proposed models provide a valid framework for analyzing multimodal data, thereby increasing precision, recall, and overall adaptiveness in dynamic real time, hence pushing the state-of-the-art in multimodal fusion and timeseries prediction. This research probably will influence those areas dependent on distributed, multimodal, and sequential data samples.