Knowledge discovery helps mitigate the shortcomings of classical machine learning, especially those so-called imbalanced, high-dimensional, and noisy data challenges. Adaptive combination of multiple models, voting and other data fusion strategies, and the incorporation of other disparate information fusion methods characterize ensemble learning, which addresses the improvement of a predictive model’s accuracy, stability, and generalization. This paper provides a summary of the important approaches to ensemble learning and their real-world uses, emphasizing challenges and opportunities for future work. This paper also discusses how ensemble learning integrates with emergent areas such as deep learning and reinforcement learning. This paper also describes the most important machine learning methods for predicting heart disease, which include decision trees, support vector machines, artificial neural networks, Naïve Bayes, random forest, and K-nearest neighbors.

Cloud–edge continuums are driving the shift of cloud-native applications from centralized data centers to latency-, mobility-, privacy-, and energy-saving applications. The serverless architecture provides an attractive “Function-as-a-Service” (FaaS) model in this transition, as it is driven by events, elastic and fine-grained, and controlled by the platform. But introducing a mix of heterogeneous edge nodes, fog/MEC resources, regional clouds, and hyperscale data centers creates a seemingly simple FaaS deployment problem to solve with a set of multi-objective orchestration challenges: runtime selection, autoscaling, cold start mitigation, placement, migration, workflow coordination, state management, trust, cost, energy, and carbon. In this article, we provide an extensive critical review of serverless functions in cloud–edge environments. While some surveys are narrowly focused on aspects of autoscaling, offloading, IoT, or security, the review brings together architectural evolution, runtime mechanisms, platform ecosystems, governance issues, sustainability issues, and emerging applications using AI. It builds a multidimensional taxonomy ranging from runtime systems, autoscaling, cold start mitigation, function placement, and offloading/migration, to workflow orchestration, state and data management, intelligent scheduling, security, sustainability, and industrial serverless platforms. It also presents a built-in conceptual model that connects application needs, runtime environment, orchestration intelligence, governance policies, and system-level results. The synthesis reveals that cloud–edge serverless systems need accountable placement, state-aware workflows, reproducible benchmarking, trustworthy orchestration, and carbon-aware lifecycle control, which can be achieved only by going beyond latency and elasticity. The paper ends with research directions on adaptive, interoperable, explainable, and sustainable serverless systems on the cloud–edge continuum.

Internet of Things (IoT) malware and intrusions generate network-observable flow patterns, but high benchmark accuracy does not by itself establish transfer to new environments. This study presents Protocol-conditioned Analysis with Behavioral Threat Identification (PA-BTI) as an evidence-bounded framework combining dataset-specific Random Forest/XGBoost detection, protocol-conditioned direction-invariant CorrAUC (dCorrAUC) diagnostics, a River streaming diagnostic, and context-aware alert prioritization. Across the primary evaluations, selected operating points reached 99.77% accuracy with 0.48% false-positive rate (FPR) on ToN-IoT and 99.44% accuracy with 0.08% FPR on a balanced Bot-IoT holdout, whereas the official NSL-KDD split reached 79.55% accuracy and 66.20% attack recall. To address testbed-artifact sensitivity, a new official UNSW-NB15 train/test ablation obtained 87.67% accuracy, 98.59% attack recall, and 25.70% FPR with all features; removing TTL/state variables changed accuracy to 87.22%, and additionally removing the ct_* windowed features changed it to 86.31%, showing no collapse but a persistently high official-split FPR. A new integrated common-flow replay propagated real UNSW detector outputs and training-only protocol evidence through the contextual layer using explicitly synthetic CTI/asset context. On a disjoint 41,166-record evaluation half, calibrated fusion did not improve context-graded nDCG@100 over confidence alone (0.610 versus 0.627), demonstrating component interaction while bounding any claim of prioritization benefit. The River diagnostic also exposed a minority-class failure mode (91.56% accuracy but attack-class F1 = 0.003). PA-BTI is therefore presented as a reproducible, bounded evaluation framework rather than evidence of cross-dataset generalization or robust portability.