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Possible drug side-effects (SEs) are usually verified by many years of repeated clinical trials. Despite the effort, some drugs are still expected to cause adverse reactions in some patients. To better predict drug SEs without having to go through the laborious processes of testing and re-testing, machine learning (ML) techniques are more and more used to uncovered patterns in drug data for such purpose...
In this article, we propose a discrete infinite logistic normal distribution (DILN) to estimate the number of states in a hidden Markov model (HMM). The HMM with the DILN priors (DILN-HMM) allows for infinite state support and model correlations between state transition probabilities. A variational Bayesian (VB) framework is proposed to infer the posterior distribution of the parameters of DILN-HMM...
With the wide spread of user-friendly mobile devices and applications, the broadband cellular network experiences a pronounced growth of traffic volume, and consumes a large amount of energy. To cope with these two challenges, it is important to understand the resource usage of base stations, which is the first step to manage the radio spectrum resources and network infrastructure efficiently. In...
Wireless sensor nodes are battery-powered communication devices. Their limited capabilities have imposed various constraints to the system design of wireless sensor networks (WSNs). These constraints are interrelated, and are usually in conflict with each other. Clustering is often used to reduce energy consumption in WSNs. However, an arbitrary selection of clustering parameters may lead to severe...
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