Song recommendation using speech emotion recognition

Bongirwar, Vrushali; Potnurwar, Archana

doi:10.53730/ijhs.v6ns1.7498

Cited by 2 publications

(1 citation statement)

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“…Implementing a decentralized network design is a groundbreaking method of improving cybersecurity in IoMT networks. Redundancy was achieved and the impact of any breaches was lessened by spreading the data over several nodes [15]. No successful data breaches were reported during this architecture was in place.…”

Section: A Related Workmentioning

confidence: 96%

A Comparative Study of Anomaly Detection Techniques for IoT Security Using Adaptive Machine Learning for IoT Threats

Alsalman

2024

IEEE Access

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Anomaly detection is a critical aspect of various applications, including security, healthcare, and network monitoring. In this study, we introduce FusionNet, an innovative ensemble model that combines the strengths of multiple machine learning algorithms, namely Random Forest, K-Nearest Neighbors, Support Vector Machine, and Multi-Layer Perceptron, for enhanced anomaly detection. FusionNet's architecture leverages the diversity of these algorithms to achieve high accuracy and precision. We evaluate FusionNet's performance on two distinct datasets, Dataset 1 and Dataset 2, and compare it with traditional machine learning models, including SVM, KNN, and RF. The results demonstrate that FusionNet consistently outperforms these models across both datasets in terms of accuracy, precision, recall, and F1 score. On Dataset 1, FusionNet achieves an accuracy of 98.5% and on Dataset 2, it attains an accuracy of 99.5%. FusionNet's remarkable ability to detect anomalies with exceptional accuracy underscores its potential for real-world applications. This study highlights the significance of FusionNet as a robust model for anomaly detection and provides insights into its superior performance over traditional models. The results emphasize the promising prospects of FusionNet in security, healthcare, and other domains where accurate anomaly detection is crucial.

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Section: A Related Workmentioning

confidence: 96%