Sarajane Marques Peres

Hand movement recognition for Brazilian Sign Language: A study using distance-based neural networks

In this paper, the vision-based hand movement recognition problem is formulated for the universe of discourse of the Brazilian Sign Language. In order to analyze this specific domain we have used the artificial neural networks models based on distance, including neural-fuzzy models. The experiments explored here show the usefulness of these models to extract helpful knowledge about the classes of movements and to support the project of adaptative recognizer modules for Libras-oriented computational tools. Using artificial neural networks architectures - Self Organizing Maps and (Fuzzy) Learning Vector Quantization, it was possible to understand the data space and to build models able to recognize hand movements performed for one or more than one specific Libras users.

LIBRAS Signals Recognition: a study with Learning Vector Quantization and Bit Signature

Learning Vector Quantization is a kind of artificial neural network with a competitive and supervised learning. This technique is commonly used to patterns recognition tasks. This artificial neural network was applied to the LIBRAS signals recognition problem, where the images representation was done with bits signatures. This is an unusual combination form and it seems promising, as inferred by the analysis of the results which are shown in this paper.

A fractal Fuzzy approach to clustering tendency analysis

A hybrid system was implemented with the combination of Fractal Dimension Theory and Fuzzy Approximate Reasoning, in order to analyze datasets. In this paper, we describe its application in the initial phase of clustering methodology: the clustering tendency analysis. The Box-Counting Algorithm is carried out on a dataset, and with its resultant curve one obtains numeric indications related to the features of the dataset. Then, a fuzzy inference system acts upon these indications and produces information which enable the analysis mentioned above.

Classification of electromyography signals using relevance vector machines and fractal dimension

Abstract Surface electromyography (EMG) signals have been studied extensively in the last years aiming at the automatic classification of hand gestures and movements as well as the early identification of latent neuromuscular disorders. In this paper, we investigate the potentials of the conjoint use of relevance vector machines (RVM) and fractal dimension (FD) for automatically identifying EMG signals related to different classes of limb motion. The adoption of FD as the mechanism for feature extraction is justified by the fact that EMG signals usually show traces of self-similarity. In particular, four well-known FD estimation methods, namely box-counting, Higuchi’s, Katz’s and Sevcik’s methods, have been considered in this study. With respect to RVM, besides the standard formulation for binary classification, we also investigate the performance of two recently proposed variants, namely constructive mRVM and top-down mRVM, that deal specifically with multiclass problems. These classifiers operate solely over the features extracted by the FD estimation methods, and since the number of such features is relatively small, the efficiency of the classifier induction process is ensured. Results of experiments conducted on a publicly available dataset involving seven distinct types of limb motions are reported whereby we assess the performance of different configurations of the proposed RVM+FD approach. Overall, the results evidence that kernel machines equipped with the FD feature values can be useful for achieving good levels of classification performance. In particular, we have empirically observed that the features extracted by the Katz’s method is of better quality than the features generated by other methods.

Process mining through artificial neural networks and support vector machines: A systematic literature review

Purpose – Process mining is a research area used to discover, monitor and improve real business processes by extracting knowledge from event logs available in process-aware information systems. The purpose of this paper is to evaluate the application of artificial neural networks (ANNs) and support vector machines (SVMs) in data mining tasks in the process mining context. The goal was to understand how these computational intelligence techniques are currently being applied in process mining. Design/methodology/approach – The authors conducted a systematic literature review with three research questions formulated to evaluate the use of ANNs and SVMs in process mining. Findings – The authors identified 11 papers as primary studies according to the criteria established in the review protocol. Most of them deal with process mining enhancement, mainly using ANNs. Regarding the data mining task, the authors identified three types of tasks used: categorical prediction (or classification); numeric prediction, co...

Gesture recognition for fingerspelling applications: an approach based on sign language cheremes

This paper presents an approach for carrying out gesture recognition for the Brazilian Sign Language Manual Alphabet. The gestural patterns are treated as a combination of three primitives, or cheremes - hand configuration, hand orientation and hand movement. The recognizer is built in a modular architecture composed by inductive reasoning modules, which use the artificial neural network Fuzzy Learning Vector Quantization; and rule-based modules. This architecture has been tested and results are presented here. Some strengths of such approach are: robustness of recognition, portability to similar contexts, extensibility of the dataset to be recognize and reduction of the vocabulary recognition problem to the recognition of its primitives.

Towards a Privacy Rule Conceptual Model for Smart Toys

A smart toy is defined as a device consisting of a physical toy component that connects to one or more toy computing services to facilitate gameplay in the cloud through networking and sensory technologies to enhance the functionality of a traditional toy. A smart toy in this context can be effectively considered an Internet of Things (IoT) with Artificial Intelligence (AI) which can provide Augmented Reality (AR) experiences to users. In this paper, the first assumption is that children do not understand the concept of privacy and the children do not know how to protect themselves online, especially in a social media and cloud environment. The second assumption is that children may disclose private information to smart toys and not be aware of the possible consequences and liabilities. This paper presents a privacy rule conceptual model with the concepts of smart toy, mobile service, device, location, and guidance with related privacy entities: purpose, recipient, obligation, and retention for smart toys. Further the paper also discusses an implementation of the prototype interface with sample scenarios for future research works.

Face recognition using Support Vector Machine and multiscale directional image representation methods: A comparative study

In recent years, human identification based on face recognition has attracted the attention of the scientific community and the general public due to its wide range of applications. A face recognition system involves three important phases: face detection, feature extraction and classification (identification and/or verification). The robustness of face recognition could be improved by treating the variations in these stages. One of the main issues in design of face recognition system is how to extract discriminative facial features. A precise extraction of a representative feature set will improve the performance of a face recognition system. Various techniques have been used to represent images efficiently, of which the most well-known and widely applied are Wavelet, Contourlet, Shearlet and Curvelet Transform. Their ability to capture localized time-frequency information of image motivates their use for feature extraction. In this paper, we conduct a systematic empirical study on these transforms as feature extractors from face images. To further reduce the feature dimensionality, we adopt Principal Component Analysis and Linear Discriminant Analysis to select the most discriminative feature sets. The performance levels delivered by each transform are contrasted in terms of the accuracy measure computed over the outputs generated by the Support Vector Machine classifier (SVM). Experimental results conducted on a publicly available database are reported whereby we observe that the Curvelet Transform followed by the Wavelet Transform significantly outperform the others according to accuracy measure calculated over the SVM classifier.

Tutorial sobre Fuzzy-c-Means e Fuzzy Learning Vector Quantization: Abordagens Híbridas para Tarefas de Agrupamento e Classificação

Neste tutorial e apresentada uma discussao sobre o algoritmo Fuzzy-c-Means e sobre as Redes Neurais Fuzzy, considerando a proposta de insercao de principios da Teoria de Conjuntos Fuzzynas abordagens de agrupamento e classificacao classicas: algoritmo c-Means e o modelo neural Learning Vector Quantization. A motivacao para a construcao de um modelo hibrido, dessa categoria, e conferir as abordagens classicas a capacidade de lidar adequadamente com aspectos de incerteza e imprecisao, comumente encontrados em problemas reais.

Self Organizing Maps and bit signature: A study applied on signal language recognition

Self organizing map (SOM) is a kind of artificial neural network with a competitive and unsupervised learning. This technique is commonly used to dataset clustering tasks and can be useful in patterns recognition problems. This paper presents an artificial neural network application to signals language recognition problem, where the image representation is given by bit signatures. The recognition results are promising and are presented in this paper. More, some analysis about the combination ldquoSOM + bit signaturerdquo improved our understanding about the characteristics of the LIBRAS signals and the conclusions are also listed in this paper.