In this paper we propose a new incremental spike sorting model that automatically eliminates refractory period violations, accounts for action potential waveform drift, and can handle “appearance” and “disappearance” of neurons. Our approach is to augment a known time-varying Dirichlet process that ties together a sequence of infinite Gaussian mixture models, one per action potential waveform o...

A constraint is a limitation or a restriction that poses a threat to the performance and efficiency of a system. This paper presented a tactical review approach to production constraints modeling. It discussed the theory of constraints (TOC) as a thinking process and continuous improvement strategy to curtail constraints in other to constantly increase the performance and efficiency of a system...

When developing fault-tolerant protocols, systems are usually modeled assuming that process failures are independent and identically distributed. In this paper, we present a system model that can represent correlated failures. We show that such a model is useful in that protocols can be made more efficient. Central to our approach is the idea of a core, which is a reliable minimal subset of pro...

Abstract Maritime inventory routing problems with load-dependent speed optimization involves determining optimal routes, as well vessel speeds and loads at these so that limits are satisfied both production consumption ports. This paper considers a variant where rates can be selected from several available alternative levels, opposed to the normal situation chosen priori. In cases, remain const...

The complexity and volume of network traffic has increased significantly due to the emergence “Internet Things” (IoT). classification accuracy is dependent on most pertinent features. In this paper, we present a hybrid feature selection method that takes into account optimization Particle Swarms (PSO) Random Forests. data collected by security firm, CIC-IDS2017, contains large number attacks in...

Granger causality (GC) is a statistical notion of causal influence based on prediction via linear vector autoregression. For Gaussian variables it equivalent to transfer entropy, an information-theoretic measure time-directed information between jointly dependent processes. We exploit such equivalence and calculate exactly the local causality, i.e., profile transferred from driver target proces...

Ontology learning refers to generating scalable ontologies based on the web of documents. It includes two main processes: first, extracting concepts and their semantic relationships; second, classifying instances based on the extracted concepts. In this paper, we proposed an effective approach called Max Similarity Min Distance Algorithm (MSMDA) to address the second process. Traditionally, the...