Federated Learning (FL) is an emerging Artificial Intelligence (AI) paradigm that enables
multiple parties to collaboratively train a model without sharing their data. With the upcoming …

The function of network intrusion detection systems (NIDS) in protecting networks from
cyberattacks is crucial. Many of the more conventional techniques rely on signature-based …

Individuals start-ups and large corporations in the healthcare sector have new opportunities
to outsource data and outsourcing computation offers to cloud computing. Although the …

Although federated learning is gaining prevalence in smart sensor networks, substantial
risks to data privacy and security persist. An improper application of federated learning …

Abstract The proposed Quantum Neural Networks (QNN) perform better than traditional
machine learning models. The escalating complexity of malware poses a significant …

As the demand for robust and secure biometric authentication systems continues to rise, this
research presents" SmartIris ML," a cutting-edge approach that harnesses the power of …

The decision tree algorithm-based load identification (DTA-LI) system's fusion data method
is crucial in the monitoring of appliance loads for the purposes of improving energy …

The enhancement of the transportation database through updated road networks is a critical
aspect with a wide range of applications. Traditional methods rely on onsite measurements …

Unmanned aerial vehicles (UAVs) have drawn much attention for various commercial and
industry applications. UAVs are a prospective contender for delivering wireless …

With the rapid proliferation of Internet of Things (IoT) devices, vulnerable environments face
increasingly sophisticated cyber threats, making intrusion detection a critical security …