8,207 research outputs found
Forecasting bus passenger flows by using a clustering-based support vector regression approach
As a significant component of the intelligent transportation system, forecasting bus passenger
flows plays a key role in resource allocation, network planning, and frequency setting. However, it remains
challenging to recognize high fluctuations, nonlinearity, and periodicity of bus passenger flows due to
varied destinations and departure times. For this reason, a novel forecasting model named as affinity
propagation-based support vector regression (AP-SVR) is proposed based on clustering and nonlinear
simulation. For the addressed approach, a clustering algorithm is first used to generate clustering-based
intervals. A support vector regression (SVR) is then exploited to forecast the passenger flow for each
cluster, with the use of particle swarm optimization (PSO) for obtaining the optimized parameters. Finally,
the prediction results of the SVR are rearranged by chronological order rearrangement. The proposed model
is tested using real bus passenger data from a bus line over four months. Experimental results demonstrate
that the proposed model performs better than other peer models in terms of absolute percentage error and
mean absolute percentage error. It is recommended that the deterministic clustering technique with stable
cluster results (AP) can improve the forecasting performance significantly.info:eu-repo/semantics/publishedVersio
Machine Learning and Integrative Analysis of Biomedical Big Data.
Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues
Dimensions of Neural-symbolic Integration - A Structured Survey
Research on integrated neural-symbolic systems has made significant progress
in the recent past. In particular the understanding of ways to deal with
symbolic knowledge within connectionist systems (also called artificial neural
networks) has reached a critical mass which enables the community to strive for
applicable implementations and use cases. Recent work has covered a great
variety of logics used in artificial intelligence and provides a multitude of
techniques for dealing with them within the context of artificial neural
networks. We present a comprehensive survey of the field of neural-symbolic
integration, including a new classification of system according to their
architectures and abilities.Comment: 28 page
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