Deep Learning of Explainable EEG Patterns as Dynamic Spatiotemporal Clusters and Rules in a Brain-Inspired Spiking Neural Network.

The paper proposes a new method for deep learning and knowledge discovery in a brain-inspired Spiking Neural Networks (SNN) architecture that enhances the model’s explainability while learning from streaming spatiotemporal brain data (STBD) in an incremental and on-line mode of operation. This led to the extraction of spatiotemporal rules from SNN models that explain why a certain decision (output prediction) was made by the model. During the learning process, the SNN created dynamic neural clusters, captured as polygons, which evolved in time and continuously changed their size and shape. The dynamic patterns of the clusters were quantitatively analyzed to identify the important STBD features that correspond to the most activated brain regions. We studied the trend of dynamically created clusters and their spike-driven events that occur together in specific space and time. The research contributes to: (1) enhanced interpretability of SNN learning behavior through dynamic neural clustering; (2) feature selection and enhanced accuracy of classification; (3) spatiotemporal rules to support model explainability; and (4) a better understanding of the dynamics in STBD in terms of feature interaction. The clustering method was applied to a case study of Electroencephalogram (EEG) data, recorded from a healthy control group (n = 21) and opiate use (n = 18) subjects while they were performing a cognitive task. The SNN models of EEG demonstrated different trends of dynamic clusters across the groups. This suggested to select a group of marker EEG features and resulted in an improved accuracy of EEG classification to 92%, when compared with all-feature classification. During learning of EEG data, the areas of neurons in the SNN model that form adjacent clusters (corresponding to neighboring EEG channels) were detected as fuzzy boundaries that explain overlapping activity of brain regions for each group of subjects

Deep Learning of Explainable EEG Patterns as Dynamic Spatiotemporal Clusters and Rules in a Brain-Inspired Spiking Neural Network

The paper proposes a new method for deep learning and knowledge discovery in a brain-inspired Spiking Neural Networks (SNN) architecture that enhances the model's explainability while learning from streaming spatiotemporal brain data (STBD) in an incremental and on-line mode of operation. This led to the extraction of spatiotemporal rules from SNN models that explain why a certain decision (output prediction) was made by the model. During the learning process, the SNN created dynamic neural clusters, captured as polygons, which evolved in time and continuously changed their size and shape. The dynamic patterns of the clusters were quantitatively analyzed to identify the important STBD features that correspond to the most activated brain regions. We studied the trend of dynamically created clusters and their spike-driven events that occur together in specific space and time. The research contributes to: (1) enhanced interpretability of SNN learning behavior through dynamic neural clustering; (2) feature selection and enhanced accuracy of classification; (3) spatiotemporal rules to support model explainability; and (4) a better understanding of the dynamics in STBD in terms of feature interaction. The clustering method was applied to a case study of Electroencephalogram (EEG) data, recorded from a healthy control group (n = 21) and opiate use (n = 18) subjects while they were performing a cognitive task. The SNN models of EEG demonstrated different trends of dynamic clusters across the groups. This suggested to select a group of marker EEG features and resulted in an improved accuracy of EEG classification to 92%, when compared with all-feature classification. During learning of EEG data, the areas of neurons in the SNN model that form adjacent clusters (corresponding to neighboring EEG channels) were detected as fuzzy boundaries that explain overlapping activity of brain regions for each group of subjects

Automated Knowledge Enrichment for Semantic Web Data

The Semantic Web is an effort to interchange unstructured data over the Web into a structured format that is processable not only by human beings but also computers. The Semantic Web creates a distributed framework to publish, query, and reuse information. The key backbones of Semantic Web are ontologies and annotations that provide semantics for raw data known as RDF data. Although there exist many Semantic Web applications, sophisticated analytical infrastructures are still lacking, preventing users from extracting the semantics attached to RDF data. Additionally, the Semantic Web data face with a wide range of data quality issues due to the distributed nature of the Semantic Web. This thesis presents three approaches based on the following purposes: (I) to express the semantics behind discovered patterns, (II) to deal with a Semantic Web data quality issue, and (III) to enrich knowledge in the Semantic Web ontologies. The following contributions have been made in this thesis. Firstly, the thesis shows the influence of relations and ontological knowledge in the process of mining hidden patterns and proposes Semantic Web Association Rule Mining (SWARM), an automated mining approach that attaches semantics to the discovered patterns. Secondly, the thesis concentrates on a data quality issue in the Semantic Web field which indicates incorrect assignments between instances and classes in the ontology. To this end, Class Assignment Detector (CAD) approach has been designed to tackle the data quality issue. Thirdly, the thesis enhances the process of ontology enrichment by generating new classes by mining instance-level and schema-level knowledge. Since ontologies are often designed before actual usage, Class Enricher (CEn) approach is developed to extract new classes which are not defined in the ontologies. All the proposed approaches have been tested over real datasets to validate their effectiveness

Quality of Service in Wireless Sensor Networks (QOS in WSN)

In this paper, we discuss about concept of Quality of Service (QoS) in Wireless Sensor Networks (WSN) and different methods to improve data security network. The most useful methods for network traffic control are Differentiated Services (DS), Integrated Services, Multi-Protocol Labeled Switching (MPLS), Resource Reservation Protocol (RSVP) and Traffic Engineering. Quality of Service is responsible for data transfer between different parts of the network and it guarantees some series of transport properties on the network [14]