Over $30 billion are wasted on unnecessary hospitalization each year, therefore it is needed to find a better quantitative way to identify patients who are mostly likely to be hospitalized and then provide them utmost...
Over $30 billion are wasted on unnecessary hospitalization each year, therefore it is needed to find a better quantitative way to identify patients who are mostly likely to be hospitalized and then provide them utmost care. As a good starting point, the objective of this paper was to develop a predictive model to predict how many days patients may spend in the hospital next year based on patients’ historical claims dataset, which is provided by the Heritage Health Prize Competition. The proposed predictive model applied the ensemble of binary classification and regression techniques. The model is evaluated on testing dataset in terms of the Root-Mean- Square-Error ( RMSE ). The best RMSE score was 0.474, and the corresponding prediction accuracy 81.9% was reasonably high. Therefore it is convincing to conclude that predictive models have the potentials to predict hospitalization and improve patients’ quality of life.
Abstract To improve the performance of classification using Support Vector Machines (SVMs) while reducing the model selection time, this paper introduces Differential Evolution, a heuristic method for model selection ...
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Abstract To improve the performance of classification using Support Vector Machines (SVMs) while reducing the model selection time, this paper introduces Differential Evolution, a heuristic method for model selection in two-class SVMs with a RBF kernel. The model selection method and related tuning algorithm are both presented. Experimental results from application to a selection of benchmark datasets for SVMs show that this method can produce an optimized classification in less time and with higher accuracy than a classical grid search. Comparison with a Particle Swarm Optimization (PSO) based alternative is also included.
The use of machine learning(ML)in the field of predicting heavy metals interaction with biochar is a promising field of research,mainly because of the growing understanding of how removal efficiency is affected by cha...
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The use of machine learning(ML)in the field of predicting heavy metals interaction with biochar is a promising field of research,mainly because of the growing understanding of how removal efficiency is affected by characteristic variables,reaction conditions and biochar *** practical application in biochar still faces large challenges,such as difficulties in data collection,inadequate algorithm development,and insufficient ***,the quantity,quality,and representation of data have a large impact on the accuracy,efficiency,and generalizability of machine learning *** this perspective,the present data descriptors,the efficiency of machine learning-aided property and performance prediction,the interpretation of underlying mechanisms and complicated relationships,and some potential ways to augment the data are discussed regarding the interactions of heavy metals with ***,future perspectives and challenges are discussed,and an enhanced model performance is proposed to reinforce the feasibility of a particular perspective.
The timely and accurate measurement of the NOx content of the power plant denitrification system is very important for the precise regulation of the amount of ammonia and the control of the NOx emission. In this paper...
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This paper presents a predictive current control strategy with active damping method applied to a current source inverter. This control strategy uses a discrete-time model of the system to predict the future value of ...
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ISBN:
(纸本)9781467371520
This paper presents a predictive current control strategy with active damping method applied to a current source inverter. This control strategy uses a discrete-time model of the system to predict the future value of the grid current for all possible current vectors generated by the inverter. According to a cost function that minimizes the errors of the predicted grid current and the reference grid current, the current vector at the next sampling time is determined. To suppress the LC resonance in the current source inverter, active damping method using virtual harmonic resistor is adopted. The performance of the proposed control method is compared with the conventional trilogic pulse width modulation control. Simulation results show that the predictive current control with active damping method has an excellent steady-state response as well as an extremely fast dynamic response compared with the conventional method.
The fuzzy vault scheme has recently become popular approaches to biometric template protection. Since the original scheme has been designed to work with unordered biometric features, such a scheme cannot effectively u...
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This book gathers high-quality papers presented at the 5th International Conference on Intelligent Computing, Communication & Devices (ICCD 2019), held in Xi'an, China on November 22–24, 2019. The contributio...
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ISBN:
(数字)9789811558870
ISBN:
(纸本)9789811558863
This book gathers high-quality papers presented at the 5th International Conference on Intelligent Computing, Communication & Devices (ICCD 2019), held in Xi'an, China on November 22–24, 2019. The contributions focus on emergent fields of intelligent computing and the development of a new generation of intelligent systems. Further, they discuss virtually all dimensions of the intelligent sciences, including intelligent computing, intelligent communication and intelligent devices.
As a kind of clean and renewable energy, wind energy is attracting more and more attention from all over the world. But the development of wind power is always hindered by the failure of downtime. The gearbox of wind ...
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Dataflow processor has shown its unique advantages in executing high performance computing applications with its communication-exposed microarchitecture. In dataflow processors, large amounts of data are directly tran...
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ISBN:
(纸本)9781538674673
Dataflow processor has shown its unique advantages in executing high performance computing applications with its communication-exposed microarchitecture. In dataflow processors, large amounts of data are directly transferred between instructions through a network-on-chip. The efficiency of data transfer is an imperative performance metric that needs to be optimized in most dataflow processors. Based on the specific features of the dataflow network, we propose a mechanism for dynamically merging the packets in the routers. By testing workloads with varying characteristics, the experiment results demonstrate that the average latency of data transfer is reduced by 11.8%, the performance of dataflow accelerator is improved by 14.0%.
One-Sided Lipschitz (OSL) fractional order modeling is a top choice for solving the stabilization issue of nonlinear systems. Despite numerous studies on the subject, there remains a gap in understanding when it comes...
One-Sided Lipschitz (OSL) fractional order modeling is a top choice for solving the stabilization issue of nonlinear systems. Despite numerous studies on the subject, there remains a gap in understanding when it comes to fractional calculus. By providing a stabilizing strategy for a certain type of OSL fractional order nonlinear systems, this study fills the gap. A numerical example demonstrating the correctness of the suggested approach and demonstrating its efficacy for the tested class.
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