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In this work, we present a performance comparison of the Multi Layer Perceptron (MLP), Support Vector Machines (SVM) and Voted Perceptron (VP) when applied to a social signal processing task. The signal processing task is in the field of computational politics where the aim is to predict the political parties of American congress members based on their response to certain questions. Using this dataset...
This paper presents the new face verification algorithm based on deep convolutional neural network. The algorithm produces face feature vectors, distance between these vectors allows to determine whether images from the same class. Comparative experimental results are given for LFW test database and modern face recognition algorithms. ROC-curve and equal error rate are used to determine the accuracy...
This electronic document is a “live” template and already defines the components of your paper [title, text, heads, etc.] in its style sheet. The paper considers the possibility and necessity of using in modern control and training systems with a natural language interface methods and mechanisms, characteristic for knowledge processing systems. This symbiosis assumes the introduction of specialized...
Previous studies have found that a significant number of bug reports are misclassified between bugs and nonbugs, and that manually classifying bug reports is a timeconsuming task. To address this problem, we propose a bug reports classification model with N-gram IDF, a theoretical extension of Inverse Document Frequency (IDF) for handling words and phrases of any length. N-gram IDF enables us to extract...
In this study, we will present a rule based fuzzy gesture recognition system where a user will interact with a spherical robot with hand gestures performed with a smart phone and the droid will respond by imitating this movements. In this context, we will take up the Gesture Recognition, Fuzzy Logic and Internet of Things (IoT) frameworks to construct such a Human-Machine Interface (HMI). In the proposed...
This paper presents a series of experiments on the classification of emergency phone conversation records using artificial neural networks (ANNs). Input data which were processed by ANNs were the features of callers and events taken from emergency phone calls. The authors analyzed four variants of classification: the groups of callers which have specified features, the groups of events which have...
Recently, multi-label classification has gained prime importance among the classification problems. The applications of classification problems has increased so rapidly that the need for efficient and accurate classifiers has become a vital requirement in the area of data mining. Multi-label classification problem is distinguished from the single label classification because of the capability to handle...
Extreme Learning Machine (ELM) is a neural network architecture with Single Layer Feed-forward Neural Network (SLFN). For meaningful results, the structure of ELM has to be optimized through the inclusion of regularization and the ℓ2 — norm based regularization is mostly used. ℓ2-norm based regularization achieves better performance than the traditional ELM. The estimate of the regularization parameter...
Data amount becomes rapidly increased in today's era. Data can be in form of text, picture, voice, and video. Social media is one factor of the data increase as everybody expresses, gives opinion, and even complains in social media. The first step is data collection used API twitter with each candidate names on Jakarta Governor Election. The collected data then became input for preprocessing step...
Every organism emits energy around it which comprises UV-radiation, EM-radiation, infrared and thermal radiation. This energy around human body represents health condition of the subject under study. These energy fields are called as aura of the body under consideration. Several types of equipments are there to capture such energy. Kirlian camera captures the distribution of energy radiation around...
An intelligent system uses machine learning algorithms to provide outputs to every input provided. The introduction of emotions in intelligent systems is required to create systems that are more similar to human beings and thus more reliable. In this paper, the idea of introducing the emotion ‘uncertainty’ in Intelligent Systems is proposed. A Semi-Automated Intelligent System is introduced in this...
Accurate dialect identification technique helps in improving the speech recognition systems that exist in most of the present day electronic devices and is also expected to help in providing new services in the field of e-health and telemedicine which is especially important for older and homebound people. The accuracy of a dialect identification system is highly dependent on its speech corpora. Therefore,...
A calibration of various microphones that have different characteristics is very difficult. This paper presents a feature extraction method as an alternative. The method provides acoustic features that are strongly robust against various characteristic transfer functions. The proposed method applies Local Binary Patterns (LBP) and Compressive Sensing (CS) which compare spectral details with spectral...
Graduate employability is an increasingly major concern for academic institutions and assessing student employability provides a way of linking student skills and employer business requirements. Enhancing student assessment methods for employability can improve their understanding about companies in order to get suitable company for them. So, enhanced employability prediction of student can help them...
Coreference resolution plays a significant role in natural language processing systems. It is the method of figuring out all the noun phrases that refer back to the identical real world entity. Several researches have been done in noun phrase coreference resolution by using certain machine learning techniques. Our paper proposes a machine learning approach using support vector machines (SVM) towards...
In the Linked Data context, identity link is one of the most important semantic links that can be established between the datasets. It specifies that different identifiers refer to the same real world object and therefore must be linked. The process of detecting these identical instances across different data repositories is referred as instance matching. This is used to connect existing data sources...
Bipolar disorder (BD) and major depressive disorder (MDD) both share depressive symptoms, so how to discriminate them in early depressive episodes is a major clinical challenge. Independent components (ICs) extracted from fMRI data have been proved to carry distinguishing information and can be used for classification. Here we extend a previous method that makes use of multiple fMRI ICs to build linear...
Identity recognition encounters with several problems especially in feature extraction and pattern classification. Electrocardiogram (ECG) is a quasi-periodic signal which has highly discriminative characteristics in a population for subject recognition. The personal identity verification in a random population using kernel-based binary and one-class Support Vector Machines (SVMs) has been considered...
Keystroke dynamics, which is a biometric characteristic that depends on typing style of users. In the past thirty years, dozens of classifiers have been proposed for distinguishing people using keystroke dynamics; many have obtained excellent results in evaluation. However, a more common case is that only normal instances are available and none of the rare classes are observed. It leads us to use...
The success of machine learning (ML) algorithms depends on the quality of data given to them. If the input data contains insufficient or irrelevant features, the accuracy of machine learning algorithm decreases. Attribute selection has a key role in creation of classification models. Based on the ‘logic behind the inference’ principle in the Nyaya school of thought, this paper proposes a new method...
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