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This paper presents novel architectures for machine learning based classifiers using stochastic logic. Two types of classifier architectures are presented. These include: linear support vector machine (SVM) and artificial neural network (ANN). Stochastic computing systems require fewer logic gates and are inherently fault-tolerant. Thus, these structures are well suited for nanoscale CMOS technologies...
Spectral clustering is a suitable technique to deal with problems involving unlabeled clusters and having a complex structure, being kernel-based approaches the most recommended ones. This work aims at demonstrating the relationship between a widely-recommended method, so-named kernel spectral clustering (KSC) and other well-known approaches, namely normalized cut clustering and kernel k-means. Such...
The Geologic resource estimation requires the accurate prediction of the regionalized variables such as ore grade at an un-sampled location with the knowledge of sparse borehole information. It plays prominent role in the decision-making process for investment and development of various mining projects and hence judicious selection of the assessment method is essential for making profitable investment...
Speaker recognition systems based on spectral features perform well in acoustically matched and noise-free conditions. Spectral features are unsuccessful to model information about the speaker at higher levels. Prosody which represents intonation, rhythm and stress of speech, better represents speaker characteristics at higher levels. Voice disguise is a common threat to automatic speaker identification...
Feature extraction addresses the problem of finding the most compact and informative set of features. To maximize the effectiveness of each single feature extraction algorithm and to develop an efficient intrusion detection system, an ensemble of Linear Discriminant Analysis (LDA) and Principle Component Analysis (PCA) feature extraction algorithms is implemented. This ensemble PCA-LDA method has...
In this paper, multimodal Deep Boltzmann Machines (DBM) is employed to learn important genes (biomarkers) on gene expression data from human carcinoma colorectal. The learning process involves gene expression data and several patient phenotypes such as lymph node and distant metastasis occurrence. The proposed framework in this paper uses multimodal DBM to train records with metastasis occurrence...
Water is classified into four status of water quality, which good condition, lightly polluted, medium polluted and heavyly polluted. The classification status of water quality is very important to know the proper use and handling. Accuracy in classification of the quality status is very important, so that both of the classification algorithm K-Nearest Neighbor (KNN) and Support Vector Machine (SVM)...
Signal peptides are significant important in targeting the translocation of integral membrane proteins and secretory proteins. Due the high similarity between the transmembrane helices and signal peptides, classifiers have limit ability to discriminate the signal peptides from the transmembrane helices. To solve this problem, the protein functional domain information is applied in this method. For...
Bag-of-visual-word (BOW) model for object recognition has attracted much attention in recent years. The ambiguity of visual words is a key issue to limit its performance. This paper presents an effective BOW scheme by introducing spatial weights, which is dependent on the saliency map. Different from conventional visual attention regions based on segmentation, this saliency map is obtained from selected...
Classification involves the learning of the mapping function that associates input samples to corresponding target label. There are two major categories of classification problems: Single-label classification and Multi-label classification. Traditional binary and multi-class classifications are sub-categories of single-label classification. Several classifiers are developed for binary, multi-class...
This paper investigates detection of patterns in brain waves while induced with mental stress. Electroencephalogram (EEG) is the most commonly used brain signal acquisition method as it is simple, economical and portable. An automatic EEG based stress recognition system is designed and implemented in this study with two effective stressors to induce different levels of mental stress. The Stroop colour-word...
This paper proposes an off-line automatic assessment system utilising novel combined feature extraction techniques. The proposed feature extraction techniques are 1) the proposed Water Reservoir, Loop, Modified Direction and Gaussian Grid Feature (WRL_MDGGF), 2) the proposed Gravity, Water Reservoir, Loop, Modified Direction and Gaussian Grid Feature (G_WRL_MDGGF). The proposed feature extraction...
We propose a Convolutional Neural Network model to learn spatial footstep features end-to-end from a floor sensor system for biometric applications. Our model's generalization performance is assessed by independent validation and evaluation datasets from the largest footstep database to date, containing nearly 20,000 footstep signals from 127 users. We report footstep recognition performance as Equal...
Face spoofing can be performed in a variety of ways such as replay attack, print attack, and mask attack to deceive an automated recognition algorithm. To mitigate the effect of spoofing attempts, face anti-spoofing approaches aim to distinguish between genuine samples and spoofed samples. The focus of this paper is to detect spoofing attempts via Haralick texture features. The proposed algorithm...
Acoustic scene classification (ASC) has attracted growing research interest in recent years. Whereas the previous work has investigated closed-set classification scenarios, the predominant ASC application is open-set in nature. The contributions of the paper are (i) the first investigation of ASC in an open-set scenario, (ii) the formulation of open-set ASC as a detection problem, (iii) a classifier...
We consider the problem of learning graphs in a sparse multiclass support vector machines framework. For such a problem, sparse graph penalty is useful to select the significant features and interpret the results. Classical ℓ1-norm learns a sparse solution without considering the structure between the features. In this paper, a structural knowledge is encoded as directed acyclic graph and a graph...
People write online documents from different personal perspectives. The competitive perspectives they hold reflect the conflicts in their fundamental stances and viewpoints. For many security-related applications, it is both beneficial and critical to identify the competitive perspectives implied in online documents. Previous work on competitive perspective identification is based on word features,...
Traffic scene object detection and recognition is extensively researched in the field of roadside assistance. Due to its importance, many methods have been proposed to solve the classification of objects in traffic and aim classification in different lighting conditions, scaling, orientation and shape of objects. Although most methods for classification are binary classification, often need multiclass...
In order to solve the problem of fault data with small sample and nonlinear in fault diagnosis and improve support vector machine, a fault diagnostic approach based on the multi-class classification method of One-Against-Rest (OAR) algorithm and decision tree is proposed combined with relevance vector machine. The above classification method modifies the current OAR algorithm using decision tree during...
Diabetes, referred to as diabetes mellitus, describes a group of metabolic diseases in which the person has high blood glucose. Possible complications that can be caused by badly controlled diabetes: Eye complications, Foot complication, Skin complications, Heart problems, Hypertension, etc. Diabetic retinopathy (DR), a common complication of diabetes, affects the blood vessels in the retina. It is...
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