Clusters driven implementation of a brain inspired model for multi-view pattern identifications

The human brain processes information in both unimodal and multimodal fashion where information is progressively captured, accumulated, abstracted and seamlessly fused. Subsequently, the fusion of multimodal inputs allows a holistic understanding of a problem. The proliferation of technology has produced various sources of electronic data and continues to do so exponentially. Finding patterns from such multi-source and multimodal data could be compared to the multimodal and multidimensional information processing in the human brain. Therefore, such brain functionality could be taken as an inspiration to develop a methodology for exploring multimodal and multi-source electronic data and further identifying multi-view patterns. In this paper, we first propose a brain inspired conceptual model that allows exploration and identification of patterns at different levels of granularity, different types of hierarchies and different types of modalities. Secondly, we present a cluster driven approach for the implementation of the proposed brain inspired model. Particularly, the Growing Self Organising Maps (GSOM) based cross-clustering approach is discussed. Furthermore, the acquisition of multi-view patterns with clusters driven implementation is demonstrated with experimental results.

Neighbourhood environments and children's social capital : investigating the pattern of relationships

The main purpose of this work was to investigate the pattern of relationships among three constructs: neighbourhood socio-physical environment, children's social interactions and their social capital. This work was designed as a two-phase mixed-methods research. Phase I included several qualitative studies to develop a scale of neighbourhood socio-physical environment, a scale of children's social interactions and a scale of children's social capital. Phase II was a cross-national survey that used these three scales to collect information from high school students in Beijing and Sydney. The main finding of this work was that there were strong and significant correlations among the three constructs. Children's assessment of their neighbourhood socio-physical environment was positively correlated with their social interactions and social capital, which indicated that children who lived in better neighbourhoods had more social interactions and larger volumes of social capital. Strong positive relationship was also found between children's social interactions and social capital, which implied that better-connected children interacted with their friends more.

Semantic trajectory based event detection and event pattern mining

Video event detection is an effective way to automatically understand the semantic content of the video. However, due to the mismatch between low-level visual features and high-level semantics, the research of video event detection encounters a number of challenges, such as how to extract the suitable information from video, how to represent the event, how to build up reasoning mechanism to infer the event according to video information. In this paper, we propose a novel event detection method. The method detects the video event based on the semantic trajectory, which is a high-level semantic description of the moving object’s trajectory in the video. The proposed method consists of three phases to transform low-level visual features to middle-level raw trajectory information and then to high-level semantic trajectory information. Event reasoning is then carried out with the assistance of semantic trajectory information and background knowledge. Additionally, to release the users’ burden in manual event definition, a method is further proposed to automatically discover the event-related semantic trajectory pattern from the sample semantic trajectories. Furthermore, in order to effectively use the discovered semantic trajectory patterns, the associative classification-based event detection framework is adopted to discover the possibly occurred event. Empirical studies show our methods can effectively and efficiently detect video events.

Respiratory pattern in awake rats : effects of motor activity and of alerting stimuli

Our aim was to assess the impact of motor activity and of arousing stimuli on respiratory rate in the awake rats. The study was performed in male adult Sprague–Dawley (SD, n = 5) and Hooded Wistar (HW, n = 5) rats instrumented for ECG telemetry. Respiratory rate was recorded using whole-body plethysmograph, with a piezoelectric sensor attached for the simultaneous assessment of motor activity. All motor activity was found to be associated with an immediate increase in respiratory rate that remained elevated for the whole duration of movement; this was reflected by: i) bimodal distribution of respiratory intervals (modes for slow peak: 336 ± 19 and 532 ± 80 ms for HW and SD, p < 0.05; modes for fast peak 128 ± 6 and 132 ± 7 ms for HW and SD, NS); and ii) a tight correlation between total movement time and total time of tachypnoea, with an R2 ranging 0.96–0.99 (n = 10, p < 0001). The extent of motor-related tachypnoea was significantly correlated with the intensity of associated movement. Mild alerting stimuli produced stereotyped tachypnoeic responses, without affecting heart rate: tapping the chamber raised respiratory rate from 117 ± 7 to 430 ± 15 cpm; sudden side move — from 134 ± 13 to 487 ± 16 cpm, and turning on lights — from 136 ± 12 to 507 ± 14 cpm (n = 10; p < 0.01 for all; no inter-strain differences). We conclude that: i) sniffing is an integral part of the generalized arousal response and does not depend on the modality of sensory stimuli; ii) tachypnoea is a sensitive index of arousal; and iii) respiratory rate is tightly correlated with motor activity.

Multi-class pattern classification in imbalanced data

The majority of multi-class pattern classification techniques are proposed for learning from balanced datasets. However, in several real-world domains, the datasets have imbalanced data distribution, where some classes of data may have few training examples compared for other classes. In this paper we present our research in learning from imbalanced multi-class data and propose a new approach, named Multi-IM, to deal with this problem. Multi-IM derives its fundamentals from the probabilistic relational technique (PRMs-IM), designed for learning from imbalanced relational data for the two-class problem. Multi-IM extends PRMs-IM to a generalized framework for multi-class imbalanced learning for both relational and non-relational domains.

On the incremental learning and recognition of the pattern of movement of multiple labelled objects in dynamic scenes

In this paper we discuss combining incremental learning and incremental recognition to classify patterns consisting of multiple objects, each represented by multiple spatio-temporal features. Importantly the technique allows for ambiguity in terms of the positions of the start and finish of the pattern. This involves a progressive classification which considers the data at each time instance in the query and thus provides a probable answer before all the query information becomes available. We present two methods that combine incremental learning and incremental recognition: a time instance method and an overall best match method.

Joint learning and dictionary construction for pattern recognition

We propose a joint representation and classification framework that achieves the dual goal of finding the most discriminative sparse overcomplete encoding and optimal classifier parameters. Formulating an optimization problem that combines the objective function of the classification with the representation error of both labeled and unlabeled data, constrained by sparsity, we propose an algorithm that alternates between solving for subsets of parameters, whilst preserving the sparsity. The method is then evaluated over two important classification problems in computer vision: object categorization of natural images using the Caltech 101 database and face recognition using the Extended Yale B face database. The results show that the proposed method is competitive against other recently proposed sparse overcomplete counterparts and considerably outperforms many recently proposed face recognition techniques when the number training samples is small.

IEF pattern classification-derived criteria for the identification of epoetin-delta in urine

Epoetin-δ (Dynepo™ Shire Pharmaceuticals, Basing stoke, UK) is a synthetic form of erythropoietin (EPO) whose resemblance with endogenous EPO makes it hard to identify using the classical identification criteria. Urine samples collected from six healthy volunteers treated with epoetin-δ injections and from a control population were immuno-purified and analyzed with the usual IEF method. On the basis of the EPO profiles integration, a linear multivariate model was computed for discriminant analysis. For each sample, a pattern classification algorithm returned a bands distribution and intensity score (bands intensity score) saying how representative this sample is of one of the two classes, positive or negative. Effort profiles were also integrated in the model. The method yielded a good sensitivity versus specificity relation and was used to determine the detection window of the molecule following multiple injections. The bands intensity score, which can be generalized to epoetin-α and epoetin-β, is proposed as an alternative criterion and a supplementary evidence for the identification of EPO abuse.

A modified fuzzy min-max neural network with a genetic-algorithm-based rule extractor for pattern classification

In this paper, a two-stage pattern classification and rule extraction system is proposed. The first stage consists of a modified fuzzy min-max (FMM) neural-network-based pattern classifier, while the second stage consists of a genetic-algorithm (GA)-based rule extractor. Fuzzy if-then rules are extracted from the modified FMM classifier, and a ??don't care?? approach is adopted by the GA rule extractor to minimize the number of features in the extracted rules. Five benchmark problems and a real medical diagnosis task are used to empirically evaluate the effectiveness of the proposed FMM-GA system. The results are analyzed and compared with other published results. In addition, the bootstrap hypothesis analysis is conducted to quantify the results of the medical diagnosis task statistically. The outcomes reveal the efficacy of FMM-GA in extracting a set of compact and yet easily comprehensible rules while maintaining a high classification performance for tackling pattern classification tasks.