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Ultra Wide-band Systems with Ensembles of Classifiers Based Latent Graph Predictor FM for Optimal Resource Prediction


Affiliations
1 Department of Electronics and Communication Engineering, Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College, India
2 Department of Electronics and Communication Engineering, Vels Institute of Science, Technology and Advanced Studies, India
3 Department of Computer and Communication Engineering, Rajalakshmi Institute of Technology, India
     

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The proliferation of Ultra Wide-Band (UWB) systems has introduced new challenges in predicting optimal resource allocation, necessitating advanced methodologies to enhance efficiency. Current resource prediction models for UWB systems often struggle to accurately forecast optimal resource allocation due to the dynamic and complex nature of the communication environment. This study aims to overcome these limitations by introducing a novel framework that integrates machine learning ensembles and latent graph predictor FM to achieve more accurate and reliable resource predictions. While various resource prediction models exist, a noticeable gap remains in achieving optimal predictions for UWB systems in dynamic scenarios. Existing models lack the adaptability and precision required for efficient resource allocation. This research bridges this gap by introducing a comprehensive approach that leverages ensembles of classifiers and latent graph predictor FM to enhance prediction accuracy. This study addresses the existing gaps in resource prediction by proposing an innovative approach that combines ensembles of classifiers with a Latent Graph Predictor FM. Our methodology involves the development of an integrated model that combines the strengths of machine learning ensembles and latent graph predictor FM. The ensemble of classifiers captures diverse patterns and features, while the latent graph predictor FM refines predictions based on latent relationships within the communication network. This dual-layered approach ensures robust and accurate resource prediction in UWB systems. The experimental results demonstrate a significant improvement in resource prediction accuracy compared to existing models. The proposed framework effectively adapts to dynamic UWB environments, providing optimal resource allocation in real-time scenarios. The study showcases the potential of ensembles of classifiers and latent graph predictor FM in addressing the challenges of resource prediction in UWB systems.

Keywords

Ultra Wide-Band Systems, Resource Prediction, Ensembles of Classifiers, Latent Graph Predictor FM, Optimal Resource Allocation.
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  • Ultra Wide-band Systems with Ensembles of Classifiers Based Latent Graph Predictor FM for Optimal Resource Prediction

Abstract Views: 155  |  PDF Views: 1

Authors

B. Ebenezer Abishek
Department of Electronics and Communication Engineering, Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College, India
A. Vijayalakshmi
Department of Electronics and Communication Engineering, Vels Institute of Science, Technology and Advanced Studies, India
Blessy Sharon Gem
Department of Electronics and Communication Engineering, Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College, India
P. Sathish Kumar
Department of Computer and Communication Engineering, Rajalakshmi Institute of Technology, India

Abstract


The proliferation of Ultra Wide-Band (UWB) systems has introduced new challenges in predicting optimal resource allocation, necessitating advanced methodologies to enhance efficiency. Current resource prediction models for UWB systems often struggle to accurately forecast optimal resource allocation due to the dynamic and complex nature of the communication environment. This study aims to overcome these limitations by introducing a novel framework that integrates machine learning ensembles and latent graph predictor FM to achieve more accurate and reliable resource predictions. While various resource prediction models exist, a noticeable gap remains in achieving optimal predictions for UWB systems in dynamic scenarios. Existing models lack the adaptability and precision required for efficient resource allocation. This research bridges this gap by introducing a comprehensive approach that leverages ensembles of classifiers and latent graph predictor FM to enhance prediction accuracy. This study addresses the existing gaps in resource prediction by proposing an innovative approach that combines ensembles of classifiers with a Latent Graph Predictor FM. Our methodology involves the development of an integrated model that combines the strengths of machine learning ensembles and latent graph predictor FM. The ensemble of classifiers captures diverse patterns and features, while the latent graph predictor FM refines predictions based on latent relationships within the communication network. This dual-layered approach ensures robust and accurate resource prediction in UWB systems. The experimental results demonstrate a significant improvement in resource prediction accuracy compared to existing models. The proposed framework effectively adapts to dynamic UWB environments, providing optimal resource allocation in real-time scenarios. The study showcases the potential of ensembles of classifiers and latent graph predictor FM in addressing the challenges of resource prediction in UWB systems.

Keywords


Ultra Wide-Band Systems, Resource Prediction, Ensembles of Classifiers, Latent Graph Predictor FM, Optimal Resource Allocation.

References