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—A novel swarm-based algorithm is proposed for the training of artificial neural networks. Training of such networks is a difficult problem that requires an effective search algorithm to find optimal weight values. While gradient-based methods, such as backpropagation, are frequently used to train multi-layer feedforward neural networks, such methods may… (More)

—A novel overlapping swarm intelligence algorithm is introduced to train the weights of an artificial neural network. Training a neural network is a difficult task that requires an effective search methodology to compute the weights along the edges of a network. The backpropagation algorithm, a gradient based method, is frequently used to train multilayer… (More)

—Spatio-temporal co-occurring patterns represent subsets of event types that occur together in both space and time. In comparison to previous work in this field, we present a general framework to identify spatio-temporal co-occurring patterns for continuously evolving spatio-temporal events that have polygon-like representations. We also propose a set of… (More)

Spatiotemporal co-occurrence patterns (STCOPs) represent the subsets of event types that occur together in both space and time. However, the discovery of STCOPs in data sets with extended spatial representations that evolve over time is computationally expensive because of the necessity to calculate interest measures to assess the co-occurrence strength,… (More)

—Abductive inference in Bayesian belief networks, also known as most probable explanation (MPE) or finding the maximum a posterior instantiation (MAP), is the task of finding the most likely joint assignment to all of the (non-evidence) variables in the network. In this paper, a novel swarm intelligence-based algorithm is introduced that efficiently finds… (More)

Pyramid Technique and iMinMax(θ) are two popular high-dimensional indexing approaches that map points in a high-dimensional space to a single-dimensional index. In this work, we perform the first independent experimental evaluation of Pyramid Technique and iMinMax(θ), and discuss in detail promising extensions for testing k-Nearest Neighbor (k NN) and range… (More)

—Abductive inference in Bayesian networks, is the problem of finding the most likely joint assignment to all non-evidence variables in the network. Such an assignment is called the most probable explanation (MPE). A novel swarm-based algorithm is proposed that finds the k-MPE of a Bayesian network. Our approach is an overlapping swarm intelligence algorithm… (More)

Spatiotemporal co-occurrence rules (STCORs) discovery is an important problem in many application domains such as weather monitoring and solar physics, which is our application focus. In this paper, we present a general framework to identify STCORs for continuously evolving spatiotemporal events that have extended spatial representations. We also analyse a… (More)