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Electroencephalography (EEG) is an emerging held of digital signal processing. EEG is electrical signals which are recorded by the sensors attached on the human scalp to detect human's brain activities along the scalp. EEG signal processing has been a technically challenging problem for researchers due to its extremely noisy nature compared to other different kinds of digital data such as voice or...
Accurate localization of the left ventricle (LV) boundary from echocardiogram images is of vital importance for the diagnosis and treatment of heart disease. Statistical shape models such as active shape models (ASM) have been commonly used to perform automatic detection of this boundary. Such mod- els perform well when there is low variability in the underlying shape subspace and an accurate initialization...
In this manuscript we propose a distributed classifier to perform inference on a person daily behaviour routine, based on multi-modal input data. The model is implemented on a social robot and allows to efficiently fuse locally perceived information with data classified remotely on a cloud. Unlike the dominant multi-class approaches, where each class is classified separately, the multi-label scheme...
A flood is an extremely dangerous disaster that can wipe away an entire city, coastline, and rural area. The flood can cause wide destrotion to property and life that has the supreme corrosive force and can be highly damaging. In order to decrease the damages caused by the flood, an Artificial Neural Network (ANN) model has been established to predict flood in Sungai Isap, Kuantan, Pahang, Malaysia...
The advent of Social Medias, Email services and other internet facilities are found helpful for a wide range of users. But some of them are interested in finding loop holes in such web based services to hinder the normal activities of common users. In this, spam Emails are one of the most disturbing activity in social network. In this context there is a need for efficient spam filters and most of...
Breast cancer has become the highest incidence of malignant tumors to global women. The breast cancer risk classification model can help reduce the incidence rate of breast cancer. For the large population of Chinese women, it is important to build an apt classification model and only the respondents in the high risk group will accept further diagnosis to filter out the breast cancer patients to lower...
A novel algorithm named Missing-Rate-Oriented Selective (MROS) algorithm - including: Most-Similar (M-S) algorithm and Attribute-Selective Imputation (ASI) approach-is proposed to achieve effective Mean Identification Rate (MIR) with minimal imputation effort for multi-classification systems in a complex and High Missing Rate (HMR) dataset. This dataset was developed from real server power supply...
This study presents an approach to dialog state tracking (DST) in an interview conversation by using the long short-term memory (LSTM) and artificial neural network (ANN). First, the techniques of word embedding are employed for word representation by using the word2vec model. Then, each input sentence is represented by a sentence hidden vector using the LSTM-based sentence model. The sentence hidden...
In this paper, a new score level fusion approach is proposed. It is based on Differential Evolution (DE) technique and Proportional Conflict Redistribution fusion rule. DE technique is used to find the best confidence factors of the belief assignments of the different modalities. The fusion of the weighted belief assignments is then performed by Proportional Conflict Redistribution combination rule...
P300-speller which relies on P300-event related potential (ERP) is an important application of the BCI system. However, the accuracy and information transmission rate were relatively low for practical use. To solve the problem, researchers focused on two aspects of paradigms and classifiers. P300-speller with familiar face paradigm achieved a better performance. In addition, Bayesian linear discriminate...
Recently, deep convolutional neural networks have set a new trend in fields of face recognition by improving the state-of-the-art performance. By using deep neural networks, much more sophisticated and high level abstracted features can be learned automatically. In this paper, we propose a method for face recognition using multi-scale convolution layer blocks and triplets of faces in unconstrained...
The automatic detection of emotions in Twitter posts is a challenging task due to the informal nature of the language used in this platform. In this paper, we propose a methodology for expanding the NRC word-emotion association lexicon for the language used in Twitter. We perform this expansion using multi-label classification of words and compare different word-level features extracted from unlabelled...
This paper proposes a novel method for offline text-independent writer identification by using convolutional neural network (CNN) and joint Bayesian, which consists of two stages, i.e. feature extraction and writer identification. In the stage of feature extraction, since a large number of data is essential to train an effective CNN model with high generalizability and the amount of handwriting is...
Many wideband wireless propagation media, including millimeter-wave and underwater acoustic channels, exhibit sparse impulse responses. Exploiting the sparse character of such channels using compressed sensing techniques can potentially lead to substantial savings in pilot overhead. In this paper, we address sequential Bayesian estimation of sparse/compressible channels with a Kalman tracking filter...
As a machine learning method under sparse Bayesian framework, classical Relevance Vector Machine (RVM) applies kernel methods to construct Radial Basis Function(RBF) networks using a least number of relevant basis functions. Compared to the well-known Support Vector Machine (SVM), the RVM provides a better sparsity, and an automatic estimation of hyperparameters. However, the performance of the original...
In the present world, there is a need of emails communication but unsolicited emails hamper such communications. The present research emphasises to build a spam classification model with/without the use of ensemble of classifiers methods have been incorporated. Through this study, the aim is to distinguish between ham emails and spam emails by making an efficient and sensitive classification model...
In this paper we address the crowdsourcing problem, where a classifier must be trained without knowing the real labels. For each sample, labels (which may not be the same) are provided by different annotators (usually with different degrees of expertise). The problem is formulated using Bayesian modeling, and considers scenarios where each annotator may label a subset of the training set samples only...
We introduce and analyse a flexible and efficient implementation of Bayesian dictionary learning for sparse coding. By placing Gaussian-inverse-Gamma hierarchical priors on the coefficients, the model can automatically determine the required sparsity level for good reconstructions, whilst also automatically learning the noise level in the data, obviating the need for heuristic methods for choosing...
This paper offers a hybrid short-term load forecasting (STLF) model using a Bayesian neural network (BNN) with a pre-processing stage consisting of a k-means clustering algorithm and time series analysis. The data clusters are time series analyzed to provide the most accurate data sets for each hour of the day. The final forecast is provided from the BNN output. California load data is used to determine...
In this paper, we focused on the problem of automatic modulation classification of digital signals. Several useful characteristic parameters which can be used for modulation analysis are extracted from spectral correlation, for different types of modulated signals have different power spectral density functions. A density estimation approach based on Support Vector Machine (SVM) is developed. Also,...
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