BackgroundThe progressive ageing in developed countries entails an increase in multimorbidity. Population-wide predictive models for adverse health outcomes are crucial to address these growing healthcare needs. The m...
详细信息
BackgroundThe progressive ageing in developed countries entails an increase in multimorbidity. Population-wide predictive models for adverse health outcomes are crucial to address these growing healthcare needs. The main objective of this study is to develop and validate a population-based prognostic model to predict the probability of unplanned hospitalization in the Basque Country, through comparing the performance of a logistic regression model and three families of machine learning *** age, sex, diagnoses and drug prescriptions previously transformed by the Johns Hopkins Adjusted Clinical Groups (ACG) System, we predict the probability of unplanned hospitalization in the Basque Country (2.2 million inhabitants) using several techniques. When dealing with non-deterministic algorithms, comparing a single model per technique is not enough to choose the best approach. Thus, we conduct 40 experiments per family of models - Random Forest, Gradient Boosting Decision Trees and Multilayer Perceptrons - and compare them to Logistic Regression. Models' performance are compared both population-wide and for the 20,000 patients with the highest predicted probabilities, as a hypothetical high-risk group to intervene *** best-performing technique is Multilayer Perceptron, followed by Gradient Boosting Decision Trees, Logistic Regression and Random Forest. Multilayer Perceptrons also have the lowest variability, around an order of magnitude less than Random Forests. Median area under the ROC curve, average precision and positive predictive value range from 0.789 to 0.802, 0.237 to 0.257 and 0.485 to 0.511, respectively. For Brier Score the median values are 0.048 for all techniques. There is some overlap between the algorithms. For instance, Gradient Boosting Decision Trees perform better than Logistic Regression more than 75% of the time, but not *** models have good global performance. The only family that is consistently superior to
The present paper compares the application of one deterministic and three non-deterministic optimization algorithms to global transformer design optimization. One deterministic optimization algorithm (Mixed Integer No...
详细信息
ISBN:
(纸本)9781467301428
The present paper compares the application of one deterministic and three non-deterministic optimization algorithms to global transformer design optimization. One deterministic optimization algorithm (Mixed Integer nonlinear Programming), is compared to three non-deterministic approaches (Harmony Search, Differential Evolution and Genetic Algorithm). The comparison yields significant conclusions on the efficiency of the algorithms and the selection of the most suitable for the transformer design optimization problem.
The present paper compares the application of one deterministic and three non-deterministic optimization algorithms to global transformer design optimization. One deterministic optimization algorithm (Mixed Integer No...
详细信息
ISBN:
(纸本)9781467301435
The present paper compares the application of one deterministic and three non-deterministic optimization algorithms to global transformer design optimization. One deterministic optimization algorithm (Mixed Integer nonlinear Programming), is compared to three nondeterministic approaches (Harmony Search, Differential Evolution and Genetic Algorithm). The comparison yields significant conclusions on the efficiency of the algorithms and the selection of the most suitable for the transformer design optimization problem.
The traditional method to solve nondeterministic-polynomial-time (NP)-hard optimization problems is to apply meta-heuristic algorithms. In contrast, Deep Q Learning (DQL) uses memory of experience and deep neural netw...
详细信息
The traditional method to solve nondeterministic-polynomial-time (NP)-hard optimization problems is to apply meta-heuristic algorithms. In contrast, Deep Q Learning (DQL) uses memory of experience and deep neural network (DNN) to choose steps and progress towards solving the problem. The dynamic time-division multiple access (DTDMA) scheme is a viable transmission method in visible light communication (VLC) systems. In DTDMA systems, the time-slots of the users are adjusted to maximize the spectral efficiency (SE) of the system. The users in a VLC network have different channel gains because of their physical locations, and the use of variable time-slots can improve the system performance. In this work, we propose a Markov decision process (MDP) model of the DTDMA-based VLC system. The MDP model integrates into deep Q learning (DQL) and provides information to it according to the behavior of the VLC system and the objective to maximize the SE. When we use the proposed MDP model in deep Q learning with experienced replay algorithm, we provide the light emitting diode (LED)-based transmitter an autonomy to solve the problem so it can adjust the time-slots of users using the data collected by device in the past. The proposed model includes definitions of the state, actions, and rewards based on the specific characteristics of the problem. Simulations show that the performance of the proposed DQL method can produce results that are competitive to the well-known metaheuristic algorithms, such as Simulated Annealing and Tabu search algorithms.
Cloud computing has evolved as the next-generation platform for hosting applications ranging from engineering to sciences, and from social networking to media content delivery. The numerous data centers, employed to p...
详细信息
Cloud computing has evolved as the next-generation platform for hosting applications ranging from engineering to sciences, and from social networking to media content delivery. The numerous data centers, employed to provide cloud services, consume large amounts of electrical power, both for their functioning and their cooling. Improving power efficiency, that is, decreasing the total power consumed, has become an increasingly important task for many data centers for reasons such as cost, infrastructural limits, and mitigating negative environmental impact. Power management is a challenging optimization problem due to the scale of modern data centers. Most published work focuses on power management in computing nodes and the cooling facility in an isolated manner. In this paper, we use a combination of server consolidation and thermal management to optimize the total power consumed by the computing nodes and the cooling facility. We describe the engineering of an evolutionary non-deterministic iterative heuristic known as simulated evolution to find the best location for each virtual machine (VM) in a data center based on computational power and data center heat recirculation model to optimize total power consumption. A "goodness" function which is related to the target objectives of the problem is defined. It guides the moves and helps traverse the search space using artificial intelligence. In the process of evolution, VMs with high goodness value have a smaller probability of getting perturbed, while those with lower goodness value may be reallocated via a compound move. Results are compared with those published in previous studies, and it is found that the proposed approach is efficient both in terms of solution quality and computational time.
暂无评论