The difficulties of continuously monitoring cardiac patients in general hospital wards are still present because of the manual charting system and the slow clinical reaction to worsening physiological state. This paper outlines an edge- and fog-based Internet of Things (IoT) healthcare system to acquire, process, and prioritize the vital signs of patients in real time to minimize the alert latency and increase the time of clinical interventions. Wearable 12-lead ECG sensors transmit physiological measurements, such as heart rate, blood pressure, and oxygen saturation, to an intelligent edge service, where preprocessing, triage by threshold, and machine learning ECG classification are performed, and selective synchronization of physiological data with a cloud backend and data delivery to the clinician are made possible by a mobile application. The proposed architecture combines a ribbon-like streaming scheme, Flask-based gateway services, and Firebase Firestore to coordinate scalable mob/cloud with the help of multi-client data dissemination. To encompass borderline clinical deterioration, which is often unnoticed by conventional threshold systems, physiological parameters are classified into normal, alarming, emergency, and a new state, average. The Pan–Tompkins++ peak detector algorithm and multiple edge-resident classifiers, such as random forest, XGBoost, decision tree, naive Bayes, K-nearest neighbor, and support vector machine, are used to analyze the ECG waveforms. Experimental analysis of PhysioNet datasets and tests in real wards prove that the ensemble models can reach the highest possible ECG classification precision of 91.96 percent and snapshot-driven mobile alerts can decrease routine patient evaluation time by several minutes, to an average of 15.23 ± 2.71 s. These results suggest that edge-centric IoT systems can be appropriate in latency-critical hospital settings and that fog-based coordination is useful in next-generation smart healthcare systems.

This research proposes a novel AI-based framework with the purpose of improving network security by combining anomaly detection by means of Feedforward Neural Networks (FNNs) and dynamic threat response by Reinforcement Learning (RL). The framework is based on a four-tier conceptual model of monitoring, feature extraction, AI analysis, and response action execution. The FNN is used for activity categorization and abnormality identification to accurately determine security threats; RL is used for real-time decision making with alert notifications and traffic blocking depending on the status of the network. The empirical analysis proves the effectiveness of the proposed framework, which obtained the accuracy of 96.5% and the cumulative average RL reward of +12.5, which indicates the ability to reduce false positive and focus on important actions. The features of the scalability and adaptability of the proposed framework were analyzed, which proved its effectiveness in addressing modern threats. This research contributes to the AI-based cybersecurity research by proposing a scalable and real-time solution to the existing gap between threat identification and dynamic response, which creates a robust defense system against new cyber threats.