Recently, many businesses and industries involved sustainability in their strategies. The demand for sustainable development in business has drawn the attention of the government and academia and emerged as a broadly ...
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This paper presents a novel technique for enhancing the allocation of resources in the charging infrastructure for e-bikes by employing deep reinforcement learning (DRL) in a context-specific manner. With the evolving...
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This paper describes problems of improving security cameras' video footage for forensic investigation. When processing records, one encounters problems with low resolution, poor lighting, or excessive distance of ...
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The Hajj is an international Islamic event that takes place in Mecca, Saudi Arabia, annually and attracts millions of pilgrims. One of the most important services during the Hajj period is pilgrims' transportation...
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The paper considers some models of a two-level hierarchical system for different-degree awareness of the Center and its subsystems. The authors investigate control procedures through the distribution of resources, the...
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The presented model of an economic system is the generalization of the Arrow-Debreu model for the dynamics case. The authors use the description techniques to develop an optimal enterprise by taking into account the d...
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Considering the immense pace in machine learning (ML) technology and related products, it may be difficult to imagine a software system, including healthcare systems, without any subsystem containing an ML model in th...
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Digital twins play a key role in the operation and management of smart factories. In some cases, an entire production line can be seen as a (composite) digital twin. The production line is often composed of multiple c...
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The rapid growth of machine learning(ML)across fields has intensified the challenge of selecting the right algorithm for specific tasks,known as the Algorithm Selection Problem(ASP).Traditional trial-and-error methods...
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The rapid growth of machine learning(ML)across fields has intensified the challenge of selecting the right algorithm for specific tasks,known as the Algorithm Selection Problem(ASP).Traditional trial-and-error methods have become impractical due to their resource *** Machine Learning(AutoML)systems automate this process,but often neglect the group structures and sparsity in meta-features,leading to inefficiencies in algorithm recommendations for classification *** paper proposes a meta-learning approach using Multivariate Sparse Group Lasso(MSGL)to address these *** method models both within-group and across-group sparsity among meta-features to manage high-dimensional data and reduce multicollinearity across eight meta-feature *** Fast Iterative Shrinkage-Thresholding Algorithm(FISTA)with adaptive restart efficiently solves the non-smooth optimization *** validation on 145 classification datasets with 17 classification algorithms shows that our meta-learning method outperforms four state-of-the-art approaches,achieving 77.18%classification accuracy,86.07%recommendation accuracy and 88.83%normalized discounted cumulative gain.
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