There are numerous energy minimisation plans that are adopted in today’s data centres (DCs). The highest important ones are those that depend on switching off unused physical machines (PMs). This is usually done by o...
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The Internet of Things (IoT) facilitates the delivery of intelligent services by sensing, gathering, processing, and exchanging data from millions of linked smart devices. The Internet of Things (IoT), which is based ...
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Classification and regression algorithms based on k-nearest neighbors (kNN) are often ranked among the top-10 Machine learning algorithms, due to their performance, flexibility, interpretability, non-parametric nature...
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Classification and regression algorithms based on k-nearest neighbors (kNN) are often ranked among the top-10 Machine learning algorithms, due to their performance, flexibility, interpretability, non-parametric nature, and computational efficiency. Nevertheless, in existing kNN algorithms, the kNN radius, which plays a major role in the quality of kNN estimates, is independent of any weights associated with the training samples in a kNN-neighborhood. This omission, besides limiting the performance and flexibility of kNN, causes difficulties in correcting for covariate shift (e.g., selection bias) in the training data, taking advantage of unlabeled data, domain adaptation and transfer learning. We propose a new weighted kNN algorithm that, given training samples, each associated with two weights, called consensus and relevance (which may depend on the query on hand as well), and a request for an estimate of the posterior at a query, works as follows. First, it determines the kNN neighborhood as the training samples within the kth relevance-weighted order statistic of the distances of the training samples from the query. Second, it uses the training samples in this neighborhood to produce the desired estimate of the posterior (output label or value) via consensus-weighted aggregation as in existing kNN rules. Furthermore, we show that kNN algorithms are affected by covariate shift, and that the commonly used sample reweighing technique does not correct covariate shift in existing kNN algorithms. We then show how to mitigate covariate shift in kNN decision rules by using instead our proposed consensus-relevance kNN algorithm with relevance weights determined by the amount of covariate shift (e.g., the ratio of sample probability densities before and after the shift). Finally, we provide experimental results, using 197 real datasets, demonstrating that the proposed approach is slightly better (in terms of F-1 score) on average than competing benchmark approaches for mit
The study address the challenge of forecasting per unit energy prices in a microgrid environment consisting of solar and hydro power resources under multi-seasonal *** deep learning techniques such as LSTM,GRU and ESN...
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The widespread adoption of renewable energy sources presents significant challenges for power system *** paper proposes a dynamic optimal power flow(DOPF)method based on reinforcement learning(RL)to ad-dress the dispa...
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The widespread adoption of renewable energy sources presents significant challenges for power system *** paper proposes a dynamic optimal power flow(DOPF)method based on reinforcement learning(RL)to ad-dress the dispatching *** proposed method consid-ers a scenario where large-scale offshore wind farms are inter-connected and have access to an onshore power grid through multiple points of common coupling(PCCs).First,the opera-tional area model of the offshore power grid at the PCCs is es-tablished by combining the prediction results and the transmis-sion capacity limit of the offshore power *** upon this,a dynamic optimization model of the power system and its RL en-vironment are constructed with the consideration of offshore power dispatching ***,an improved algorithm based on the conditional generative adversarial network(CGAN)and the soft actor-critic(SAC)algorithm is *** analyzing an improved IEEE 118-node system,the proposed method proves to have the advantage of economy over a longer *** resulting strategy satisfies power system opera-tion constraints,effectively addressing the constraint problem of action space of RL,and it has the added benefit of faster so-lution speeds.
In recent years,intelligent robots are extensively applied in the field of the industry and intelligent rehabilitation,wherein the human-robot interaction(HRI)control strategy is a momentous part that needs to be ***,...
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In recent years,intelligent robots are extensively applied in the field of the industry and intelligent rehabilitation,wherein the human-robot interaction(HRI)control strategy is a momentous part that needs to be ***,the efficacy and robustness of the HRI control algorithm in the presence of unknown external disturbances deserve to be *** deal with these urgent issues,in this study,artificial systems,computational experiments and a parallel execution intelligent control framework are constructed for the HRI *** upper limb-robotic exoskeleton system is re-modelled as an artificial *** on surface electromyogram-based subject's active motion intention in the practical system,a non-convex function activated anti-disturbance zeroing neurodynamic(NC-ADZND)controller is devised in the artificial system for parallel interaction and HRI control with the practical ***,the linear activation function-based zeroing neurodynamic(LAF-ZND)controller and proportionalderivative(posterior deltoid(PD))controller are presented and *** results substantiate the global convergence and robustness of the proposed controller in the presence of different external *** addition,the simulation results verify that the NC-ADZND controller is better than the LAF-ZND and the PD controllers in respect of convergence order and anti-disturbance characteristics.
The right partner and high innovation speed are crucial for a successful research and development (R&D) alliance in the high-tech industry. Does homogeneity or heterogeneity between partners benefit innovation spe...
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Trojan detection from network traffic data is crucial for safeguarding networks against covert infiltration and potential data breaches. Deep learning (DL) techniques can play a pivotal role in detecting trojans from ...
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An Internet of Mobile Things (IoMT) refers to an internetworked group of pervasive devices that coordinate their motion and task execution through frequent status and data exchange. An IoMT could be serving critical a...
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Machine learning algorithms generally assume that the data are balanced in nature. However, medical datasets suffer from the curse of dimensionality and class imbalance problems. The medical datasets are obtained from...
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Machine learning algorithms generally assume that the data are balanced in nature. However, medical datasets suffer from the curse of dimensionality and class imbalance problems. The medical datasets are obtained from the patient information which creates an imbalance in class distribution as the number of normal persons is more than the number of patients and contains a large number of features to represent a sample. It tends to the machine learning algorithms biased toward the majority class which degrades their classification performance for minority class samples and increases the computation overhead. Therefore, oversampling, feature selection and feature weighting-based four strategies are proposed to deal with the problems of class imbalance and high dimensionality. The key idea behind the proposed strategies is to generate a balanced sample space along with the optimal weighted feature space of the most relevant and discriminative features. The Synthetic Minority Oversampling Technique is utilized to generate the synthetic minority class samples and reduce the bias toward the majority class. An Improved Elephant Herding Optimization algorithm is applied to select the optimal features and weights for reducing the computation overhead and improving the interpretation ability of the learning algorithms by providing weights to relevant features. In addition, thirteen methods are developed from the proposed strategies to deal with the problems of high-dimensionality and imbalanced data. The optimized k-Nearest Neighbor (k-NN) learning algorithm is utilized to perform classification. The performance of the proposed methods is evaluated and compared for sixteen high-dimensional imbalanced medical datasets. Further, Freidman’s mean rank test is applied to show the statistical difference between the proposed methods. Experimental and statistical results show that the proposed Feature Weighting followed by the Feature Selection (FW–FS) method performed significantly b
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