Over the past few years, AI has emerged as a formidable asset in the battle against spam. AI approaches have demonstrated their potential in improving spam detection in social systems. The importance of AI methods lie...
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We propose a network service as a solution for video conference applications by constructing network layer routing strategies. Our approach takes into account the characteristics of conferencing flows, addresses vario...
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This paper examines the use of supervised machine learning to construct a digital twin model replicating a physical plant. An inverted pendulum simulation has been used as a case study. A comparative study was conduct...
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Goal-oriented requirements engineering (GORE) for Systems of Systems (SoS) includes combining individual operational systems local goals to achieve higher-level goals. GORE offers a structured approach to managing com...
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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.
Drug discovery is an expensive and risky process. To combat the challenges in drug discovery, an increasing number of researchers and pharmaceutical companies recognize the benefits of utilizing computational techniqu...
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Drug discovery is an expensive and risky process. To combat the challenges in drug discovery, an increasing number of researchers and pharmaceutical companies recognize the benefits of utilizing computational techniques. Evolutionary computation (EC) offers promise as most drug discovery problems are essentially complex optimization problems beyond conventional optimization algorithms. EC methods have been widely applied to solve these complex optimization problems especially in lead com-pound generation and molecular virtual evaluation, substantially speeding up the process of drug discovery and development. This article presents a comprehensive survey of EC-based drug discovery methods. Particularly, a new taxonomy of the methods is provided and the advantages and limitations of the methods are reviewed. In addition, the potential future directions of EC-based drug discovery are discussed and the publicly available resources including databases and computational tools are compiled for the convenience of researchers seeking to pursue this field. IEEE
Context-aware fire detection is a significant task in the era of new urban monitoring. Severe damage might result from fire events. To minimize the occurrence of these events, timely detection of fire accidents is nec...
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Preventing mechanical faults in motors is often impossible, early detection of air gap eccentricity faults in induction motors is critical in preventing damage to the machine. Therefore, designing a reliable, effectiv...
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This paper has introduced studies on applying reinforcement learning to MAC protocols in wireless networks to enhance network performance. As services and users demanding improved network performance increase, using r...
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In this paper, the method for knowledge graph completion based on using multi-hop reasoning is proposed. The relevance of the problem is due to the widespread use of large sparse knowledge graphs with incomplete data....
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