The increasing dependence and demands on cloud infrastructure have brought to light challenges associated with cloud instance pricing. The often unpredictable nature of demand as well as changing costs of supplying a reliable instance can leave companies struggling to appropriately budget to support a healthy cash flow while maintaining operating costs. This work explores the potential of multi-headed recurrent architectures to forecast cloud instance prices based on historical and instance data. Two architectures are explored, long short-term memory (LSTM) and gated recurrent unit (GRU) networks. A modified optimizer is introduced and tested on a publicly available Amazon elastic compute cloud dataset. The GRU model, enhanced by the proposed modified approach, had the most impressive outcomes with an MAE score of 0.000801. Results have undergone meticulous statistical validation with the best-performing models further analyzed using explainable artificial intelligence techniques to provide further insight into model reasoning and information on feature importance.

Accurate evaluation of a cardiac patient’s risk at the point of hospital entry is critical for efficient triage and ensuring timely, suitable medical intervention. This study aims to forecast a range of clinical outcomes by leveraging admission data from a cardiac care unit, utilizing a refined and optimized machine learning approach. This research introduces a hybrid architecture that integrates convolutional neural networks (CNNs) with advanced machine learning classifiers, namely light gradient boosting machine (LightGBM) and categorical boosting (CatBoost), further enhanced through metaheuristic optimization techniques to maximize their performance. The proposed two-tiered design organizes feature extraction and final decision modeling into a coherent pipeline tailored for multi-class hospital admission classification. A comprehensive evaluation using a real-world hospital admission dataset demonstrates the framework’s effectiveness on a real-world, publicly available hospital admission dataset, supporting its utility for multi-class cardiac outcome prediction. Three experiments were conducted using publicly available datasets, where the best-performing models achieved a peak classification accuracy of 99.79%. Furthermore, explainable AI techniques were employed to interpret model predictions, offering actionable insights that can guide future data acquisition and strengthen the accurate classification of cardiac patients.