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Medical literature have recognized physical activity as a key factor for a healthy life due to its remarkable benefits. However, there is a great variety of physical activities and not all of them have the same effects on health nor require the same effort. As a result, and due to the ubiquity of commodity devices able to track users' motion, there is an increasing interest on performing activity...
With the increasing risk of data leakage, information guards have emerged as a novel concept in the field of security which bears similarity to spam filter that examine the content of the exchanged messages. A guard is defined as a high-assurance device used to control the information flow, typically from a domain with a "high" level of confidentiality, such as a corporate or military network,...
This paper evaluates the potential of convolutional neural networks in classifying short audio clips of environmental sounds. A deep model consisting of 2 convolutional layers with max-pooling and 2 fully connected layers is trained on a low level representation of audio data (segmented spectrograms) with deltas. The accuracy of the network is evaluated on 3 public datasets of environmental and urban...
We investigate the neural correlates of visual working memory using electroencephalography (EEG). Our objective is to develop a cognitive Brain-Computer Interface (BCI) able to monitor visual working memory load in real-time. A system with these properties would eventually have different applications, such as training, rehabilitation, or safety while operating dangerous machinery. The BCI performances...
Cry segmentation is an essential preprocessing step in any infant crying diagnosis system. Besides crying sounds consisting of expiration phases followed by short periods of inspiration episodes, each recording of newborn cries also includes silence sections as well as other sounds such as speech of caregivers, noise and sound of medical equipments. This paper is devoted to a newly developed Empirical...
A method for sentiment polarity assignment for textual content written in Polish using supervised machine learning approach with transfer learning scheme is proposed in the paper. It has been shown that performing simple natural language processing steps prior to classification, provides inspiring results without redundant computation overhead. The documents containing subjective opinions were classified...
Depth of anesthesia is a matter of great importance in surgery. Determination of depth of anesthesia is a time consuming and difficult task carried out by experts. This study aims to decide a method that can classify EEG data automatically with a high accuracy and, so will help the experts for determination process. This study consists of three stages: feature extraction of EEG signals, feature selection,...
Fingerprinting based positioning is commonly used for indoor positioning. In this method, initially a radio map is created using Received Signal Strength (RSS) values that are measured from predefined reference points. During the positioning, the best match between the observed RSS values and existing RSS values in the radio map is established as the predicted position. In the positioning literature,...
In this paper, we propose a new framework for remote sensing images classification based on group sparse representations. It is well known that it is very difficult to find a suitable sparse representation for remote sensing images because of complicated ground features. Here a remote sensing image is deemed a combination of sub-images of smooth, edges and point like components. Since each domain...
Keystroke dynamics authentication is not as widely used compared to other biometric systems. In recent years, keystroke dynamic authentication systems have gained interest because of low cost and integration with existing security systems. Many different methods have been proposed for data collection, feature representation, classification, and performance evaluation. The work presents a detailed...
The emerging new data types bring tremendous challenges to data mining. There is an enormous amount of high-dimensional class-imbalanced data in different fields. In this case, traditional classification methods are not appropriate because they are prone to ensure the accuracy of the majority class. Meanwhile, the curse of dimensionality makes situations more complicated. Finding a complicated classifier...
Electroencephalographic (EEG) patterns are electrical signals generated in association with neural activities. Most anomalies in brain functioning manifest with their signature characteristics in EEG pattern. Epileptic seizure, which is a brain abnormality well-studied through EEG analysis, is an abnormal harmonious neural activity in the brain characterized by the presence of spikes in EEG. An automated...
This paper describes, and illustrates using documented applications, a general framework methodology for wide-area forest and land use mapping and change detection using Synthetic Aperture Radar (SAR) remote sensing. Consideration is given to implementation of the SAR-based methodology using both commercial and free/open-source software. Our experience shows that constructing a complete processing...
Expert systems for classification tasks in medical diagnosis systems require two properties. The true positives should be very high, as well as the true negatives, i.e. the system should correctly catch those who are ill, and correctly dismiss those who are healthy. The multi-modal evolutionary classifier uses a genetic algorithm to learn a reference vector for each class, and classification is done...
NeuroinformaticsNatural Language Processing (NeuroNLP) relies on clustering and classification for information categorization of biologically relevant extraction targets and for interconnections to knowledge-related patterns in event and text mined datasets. The accuracy of machine learning algorithms depended on quality of text-mined data while efficacy relied on the context of the choice of techniques...
Malware analysis on the Android platform has been an important issue as the platform became prevalent. The paper proposes a malware detection approach based on static analysis and machine learning techniques. By conducting SVM training on two different feature sets, malicious-preferred features and normal-preferred features, we built a hybrid-model classifier to improve the detection accuracy. With...
The sequential covering strategy has been and still is a very common way to develop rule learning algorithms. This strategy follows a greedy procedure to learn rules, where, after each step one rule is obtained. Recently, we proposed a new sequential covering strategy that allowed the review of previously learned knowledge during the learning process itself. This review of knowledge allowed the algorithm...
We propose to address the handwritten digits recognition (HWDR) problem by using a two-dimensional (2-D) discrete cosine transform (DCT) based sparse principal component analysis (PCA) algorithm for fast classification. The gain of processing speed is achieved by utilizing the ability of 2-D DCT for energy compaction and signal decorrelation. The proposed algorithm was applied to the mixed national...
Generalizing hypotheses based on the past data in order to predict the future is the essential core of human learning. Various successful methods and techniques have been developed so far that perform some sort of classification of current data in order to predict future unseen cases. Multi class classification problems are among them as well. In many domains in spite of these automatic techniques,...
With the widely application of high-resolution remote sensing images, its classification has attracted a lot of attention. Most classification methods focus on various combination of features and ignore the similarities between different categories. In this paper we present a modification by combining ScSPM [1] with a dictionary learning method DL-COPAR [2], which separates the particularity and commonality...
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