Driven by the success of transformer models in representation learning across different Machine Learning fields, this study explores the development of a transformer-based Algorithm Selection (AS) model. We demonstrat...
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Wireless sensor networks are widely used in areas such as monitoring of complex environments. Nodes consume energy due to sensing and transmission, and there is a hot-spot problem. Studies have shown that clustering i...
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The long-term presence of antibiotics in the aquatic environment will affect ecology and human health. Techniques for determining antibiotics are often time-consuming, labor-intensive and costly, and it is desirable t...
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The long-term presence of antibiotics in the aquatic environment will affect ecology and human health. Techniques for determining antibiotics are often time-consuming, labor-intensive and costly, and it is desirable to seek new methods to achieve rapid prediction of antibiotics. Many scholars have shown the effectiveness of machine learning in water quality prediction, however, its effectiveness in predicting antibiotic concentrations in the aquatic environment remains inconclusive. Given that conventional water quality indicators directly or indirectly influence antibiotic concentrations, we explored the feasibility of predicting ciprofloxacin (CFX) concentrations based on conventional water quality indicators with the help of three commonly used machine learning algorithms and two parameter optimization algorithms. Then, we evaluated and determined the best model using four commonly used model performance evaluation metrics. The evaluation results showed that the generalized regression neural network (GRNN) model optimized by particle swarm optimization (PSO) had the best prediction among all the models under the conditions of six input variables, namely COD, NH4+-N, DO, WT, TN, and pH. The performance evaluations were R2= 0.936, NSE= 0.915, RMSE= 3.150 ng/L, and MAPE= 30.909 %. Overall, the CFX prediction models met the requirements for antibiotic concentration prediction accuracy, offering a potential indirect method for predicting antibiotic concentrations in water quality management.
Wireless sensor network (WSN) is widely used in fields such as marine environmental protection, and their nodes are usually deployed randomly, which can easily lead to low coverage in the monitoring area. On the basis...
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Based on the graph theory and the complex network analysis method, the stability of transmission and distribution system (TDS) is deeply studied in this study. Firstly, by establishing the topological structure model ...
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The Dynamic Resource Allocation Multi-Objective optimization Algorithm (MOEA/D-DRA) is a method for solving multi-objective optimization problems (MOPs). This algorithm enhances the performance of the original MOEA/D ...
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Large-scale machine learning problems make the cost of hyperparameter tuning ever more prohibitive. This creates a need for algorithms that can tune themselves on-the-fly. We formalize the notion of "tuning-free&...
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Large-scale machine learning problems make the cost of hyperparameter tuning ever more prohibitive. This creates a need for algorithms that can tune themselves on-the-fly. We formalize the notion of "tuning-free" algorithms that can match the performance of optimally-tuned optimization algorithms up to polylogarithmic factors given only loose hints on the relevant problem parameters. We consider in particular algorithms that can match optimally-tuned Stochastic Gradient Descent (SGD). When the domain of optimization is bounded, we show tuning-free matching of SGD is possible and achieved by several existing algorithms. We prove that for the task of minimizing a convex and smooth or Lipschitz function over an unbounded domain, tuning-free optimization is impossible. We discuss conditions under which tuning-free optimization is possible even over unbounded domains. In particular, we show that the recently proposed DoG and DoWG algorithms are tuning-free when the noise distribution is sufficiently well-behaved. For the task of finding a stationary point of a smooth and potentially nonconvex function, we give a variant of SGD that matches the best-known high-probability convergence rate for tuned SGD at only an additional polylogarithmic cost. However, we also give an impossibility result that shows no algorithm can hope to match the optimal expected convergence rate for tuned SGD with high probability. Copyright 2024 by the author(s)
Bandit convex optimization (BCO) is a general framework for online decision making under uncertainty. While tight regret bounds for general convex losses have been established, existing algorithms achieving these boun...
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Various methods have been developed to optimize Distributed Generation (DG) placement and sizing in the power system network. While better advanced algorithms for DG optimization are being continuously developed, simp...
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Classical convergence analyses for optimization algorithms rely on the widely-adopted uniform smoothness assumption. However, recent experimental studies have demonstrated that many machine learning problems exhibit n...
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