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The data that moves across social relations, biology, and recommendation engines has given and continues to provide the standard models in machine learning a serious challenge, as it is highly structured (in terms of topology), heterogeneous, and mostly scale-constrained. The present study proposes an advanced multi-objective framework, named a machine learning network system (MLNS), that overcomes these limitations by simultaneously incorporating transformer-based features, Pareto-optimal optimization, and federated learning. The generalization of the learned model under variable conditions is also enhanced. At the same time, the computational complexity is reduced, as evidenced by the MLNS architecture, which enables effective learning with graph-structured inputs. Furthermore, this approach underwent empirical assessment utilizing extensive benchmark datasets, including ImageNet and MS COCO. The results substantiated its superiority over contemporary methodologies in terms of metrics such as accuracy, precision, recall, and F1-score, achieving up to a 16% enhancement in performance, even under constrained computing environments. The efficiency of the framework was also explained by the all-embracive nature of ablation research and the examination of parameter sensitivity. The findings demonstrate the relevance of module design and the flexibility of learning in the architecture to enhance model stability. Theoretical results that are more rigorous provide convergence and optimal resource guarantees, which have been proven to guarantee that MLNS can be implemented in a real-time and distributed system. The work presents high-performance machine learning on networks, whose interpretation and scaling are facilitated by addressing the background theoretical and practical challenges in machine learning on networks; hence, the work can be applied extensively. Multimodal learning and the automatic adaptation of the system are also future directions, which in turn create new studies of intelligent next-generation networks.

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Machine Learning Networks System (MLNS) A Multi-objective Machine Learning Framework for Network Structured Data

  • 会议论文

  • 发表日期：

    2026年

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  • Kranthi Kumar Singamaneni,
  • Bomma Ramakrishna,
  • M. S. V. K. V. Prasad,
  • Ramesh Babu Pittala,
  • Gandham Naga Visweswara Rao,
  • Medikonda Asha Kiran

摘要

The data that moves across social relations, biology, and recommendation engines has given and continues to provide the standard models in machine learning a serious challenge, as it is highly structured (in terms of topology), heterogeneous, and mostly scale-constrained. The present study proposes an advanced multi-objective framework, named a machine learning network system (MLNS), that overcomes these limitations by simultaneously incorporating transformer-based features, Pareto-optimal optimization, and federated learning. The generalization of the learned model under variable conditions is also enhanced. At the same time, the computational complexity is reduced, as evidenced by the MLNS architecture, which enables effective learning with graph-structured inputs. Furthermore, this approach underwent empirical assessment utilizing extensive benchmark datasets, including ImageNet and MS COCO. The results substantiated its superiority over contemporary methodologies in terms of metrics such as accuracy, precision, recall, and F1-score, achieving up to a 16% enhancement in performance, even under constrained computing environments. The efficiency of the framework was also explained by the all-embracive nature of ablation research and the examination of parameter sensitivity. The findings demonstrate the relevance of module design and the flexibility of learning in the architecture to enhance model stability. Theoretical results that are more rigorous provide convergence and optimal resource guarantees, which have been proven to guarantee that MLNS can be implemented in a real-time and distributed system. The work presents high-performance machine learning on networks, whose interpretation and scaling are facilitated by addressing the background theoretical and practical challenges in machine learning on networks; hence, the work can be applied extensively. Multimodal learning and the automatic adaptation of the system are also future directions, which in turn create new studies of intelligent next-generation networks.