ABSTRACT
Wi-Fi has shown enormous potential for indoor localization because of its wide utilization and availability. Enabling the use of Wi-Fi for indoor localization necessitates the construction of a fingerprint and the adoption of a learning algorithm. The goal is to enable the use of the fingerprint in training the classifiers for predicting locations. Existing models of machine learning Wi-Fi-based localization are brought from machine learning and modified to accommodate for practical aspects that occur in indoor localization. The performance of these models varies depending on their effectiveness in handling and/or considering specific characteristics and the nature of indoor localization behavior. One common behavior in the indoor navigation of people is its cyclic dynamic nature. To the best of our knowledge, no existing machine learning model for Wi-Fi indoor localization exploits cyclic dynamic behavior for improving localization prediction. This study modifies the widely popular online sequential extreme learning machine (OSELM) to exploit cyclic dynamic behavior for achieving improved localization results. Our new model is called knowledge preserving OSELM (KP-OSELM). Experimental results conducted on the two popular datasets TampereU and UJIndoorLoc conclude that KP-OSELM outperforms benchmark models in terms of accuracy and stability. The last achieved accuracy was 92.74% for TampereU and 72.99% for UJIndoorLoc.
ABSTRACT
UNLABELLED: Phylodynamic analysis of genome-wide single-nucleotide polymorphism (SNP) data is a powerful tool to investigate underlying evolutionary processes of bacterial epidemics. The method was applied to investigate a collection of 65 clinical and environmental isolates of Vibrio cholerae from Haiti collected between 2010 and 2012. Characterization of isolates recovered from environmental samples identified a total of four toxigenic V. cholerae O1 isolates, four non-O1/O139 isolates, and a novel nontoxigenic V. cholerae O1 isolate with the classical tcpA gene. Phylogenies of strains were inferred from genome-wide SNPs using coalescent-based demographic models within a Bayesian framework. A close phylogenetic relationship between clinical and environmental toxigenic V. cholerae O1 strains was observed. As cholera spread throughout Haiti between October 2010 and August 2012, the population size initially increased and then fluctuated over time. Selection analysis along internal branches of the phylogeny showed a steady accumulation of synonymous substitutions and a progressive increase of nonsynonymous substitutions over time, suggesting diversification likely was driven by positive selection. Short-term accumulation of nonsynonymous substitutions driven by selection may have significant implications for virulence, transmission dynamics, and even vaccine efficacy. IMPORTANCE: Cholera, a dehydrating diarrheal disease caused by toxigenic strains of the bacterium Vibrio cholerae, emerged in 2010 in Haiti, a country where there were no available records on cholera over the past 100 years. While devastating in terms of morbidity and mortality, the outbreak provided a unique opportunity to study the evolutionary dynamics of V. cholerae and its environmental presence. The present study expands on previous work and provides an in-depth phylodynamic analysis inferred from genome-wide single nucleotide polymorphisms of clinical and environmental strains from dispersed geographic settings in Haiti over a 2-year period. Our results indicate that even during such a short time scale, V. cholerae in Haiti has undergone evolution and diversification driven by positive selection, which may have implications for understanding the global clinical and epidemiological patterns of the disease. Furthermore, the continued presence of the epidemic strain in Haitian aquatic environments has implications for transmission.
