13,117 research outputs found
Big data analytics:Computational intelligence techniques and application areas
Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment
A Deep Spatio-Temporal Fuzzy Neural Network for Passenger Demand Prediction
In spite of its importance, passenger demand prediction is a highly
challenging problem, because the demand is simultaneously influenced by the
complex interactions among many spatial and temporal factors and other external
factors such as weather. To address this problem, we propose a Spatio-TEmporal
Fuzzy neural Network (STEF-Net) to accurately predict passenger demands
incorporating the complex interactions of all known important factors. We
design an end-to-end learning framework with different neural networks modeling
different factors. Specifically, we propose to capture spatio-temporal feature
interactions via a convolutional long short-term memory network and model
external factors via a fuzzy neural network that handles data uncertainty
significantly better than deterministic methods. To keep the temporal relations
when fusing two networks and emphasize discriminative spatio-temporal feature
interactions, we employ a novel feature fusion method with a convolution
operation and an attention layer. As far as we know, our work is the first to
fuse a deep recurrent neural network and a fuzzy neural network to model
complex spatial-temporal feature interactions with additional uncertain input
features for predictive learning. Experiments on a large-scale real-world
dataset show that our model achieves more than 10% improvement over the
state-of-the-art approaches.Comment: https://epubs.siam.org/doi/abs/10.1137/1.9781611975673.1
A survey of machine learning techniques applied to self organizing cellular networks
In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future
- …