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NB-UVB Phototherapy is one of the most common treatments administrated by dermatologists for psoriasis patients. Although in general, the treatment results in improving the condition, it also can worsen it. If a model can predict the treatment response before hand, the dermatologists can adjust the treatment accordingly. In this paper, we use data mining techniques and conduct four experiments. The...
Twitter is a popular microblogging service that allows its users to view and share limited character messages (known as “tweets”) with the public. This paper proposes a tweet sentiment classification framework which pre-processes information from Emoticon and Emoji in such way that their textual representation is included to enrich the tweet. Once the tweets are pre-processed, a hybrid computational...
One of the most important ways to explore the information in hyperspectral images (HSIs) is accurate classification of targets. Deep learning algorithm has made a great breakthrough in many areas due to its strong ability of data mining. Typical deep learning models such as convolutional neural network (CNN), deep belief network (DBN) and so on, not only combines the advantages of unsupervised and...
Accurate and efficient assessment of barley quality is crucial for brewery industry. Currently, such the assessment is performed by a qualified expert, is expensive, time-consuming and may yield unreproducible results. We introduce a barley ontology (BO) to formalize the expert's knowledge and to involve information from industry standards. The BO specifies quality classes of malting barley, their...
Remote sensing is the acquisition of information about an object or phenomenon without making physical contact with the object. Remote sensing is used in numerous fields, including geography, land surveying and most Earth Science disciplines. In supervised classification, all of the feature extraction methods try to increase the accuracy of classification and simultaneously time of computation. At...
The aim of this paper is to compare the performance of different kernel of classification support vector machine classification for classifying the physical daily living activities. Thirty subjects from a database performed activities such as walking, sitting, standing, laying, walking upstairs and downstairs. Inertial sensors signals ((accelerometer, gyroscope and magnetometer) from the smartphone...
Neuroscience researchers have a keen interest in finding the connection between various brain regions of an organism. Researchers all across the globe are finding new connections everyday and it is very difficult to keep track of all those, so it is important to create a centralized system which is able to give the relation between brain entities. Databases like PubMed contains abstracts and references...
Based on the spectral data from SDSS, Kernel Support Vector Machines (K-SVM) is applied to classify quasars from other celestial body. Firstly, the basic theory of the SVM(Support Vector Machine) with relaxation factor and kernel function is introduced. Then, the main parameters are designed and selected. Finally, the method is applied to the classification and identification of the quasars. The classification...
Development of smart cities has grasped much attention in research community and industry as well. Smart healthcare, communication, infrastructure are required for the development of smart cities. Security is one of the major concern in the development of smart cities. Automatic surveillance helps in boosting security in multiple areas like traffic, hospitals, schools, and industries etc. Video camera...
This paper has proposed gait recognition approach for analyzing and classifying human identification under carrying a bag and wearing a clothing thus improving recognition performances. The proposed method is based on detail wavelet features extracted from the Haar-wavelet decomposition of dynamic areas in the Gait Energy Image (GEI). Spectral Regression Kernel Discriminant Analysis (SRKDA) is then...
In this work, we develop a new framework to combine ensemble learning and composite kernel learning for hyperspectral image classification. We refer it as the multiple composite kernel learning, which is based on an iterative architecture. More specifically, in each iteration, we use the rotation-based ensemble to create rotation matrix, which is used to generate rotated features for both spectral...
Extinction profile (EP) is an effective feature extraction method which can well preserve the geometrical characteristics of a hyperspectral image (HSI) and by extracting the EP from first three independent components (ICs) of an HSI, three correlated and complementary groups of EP features can be constructed. In this paper, an EPs fusion (EPs-F) strategy is proposed for HSI classification by exploring...
In this paper, we propose a kernel low-rank multitask learning (KL-MTL) method to handle multiple features from the variational mode decomposition (VMD) domain for hyperspectral (HSI) classification. Core ideas of the proposed method are twofold: 1) a non-recursive VMD method is applied to extract various features (i.e. intrinsic mode functions (IMFs)) of the original data concurrently; 2) KL-MTL...
These days, a lot number of elderly people need health care which may cause huge financial costs, especially in formal case. Machine Learning and the profound achievements in sensing technology provide the opportunities to monitor people living independently at home and can detect a distress situation affordably. Although there are some approaches to do recognize activities for this purpose, but there...
Tuberculosis is one of the top ten causes of death worldwide. Although this disease is curable and preventable, yet many new tuberculosis cases still occur especially in developing countries. Many low-income families cannot afford the medical diagnosis for tuberculosis. Therefore, this paper proposes an initial screening for tuberculosis infection using a data mining approach. In this paper, the initial...
Yield estimation is becoming a challenging task for circuits that are replicated in millions of instances on a large design (High Replication Circuits, HRC) such as SRAMs and flip flops. This is because a rare event in a circuit cell may have a large impact on the system yield. To achieve high yield in HRC, the failure probability of the individual cell is requested to be very small. Thus the number...
In this work, a simple method for separation between normal and abnormal heart sounds (Phonocardiogram) is presented. Mel-Frequency Cepstral Coefficients (MFCC) are extracted from two different datasets of heartbeats. Several Classifiers, such as, Support Vectors Machine (SVM), K-Nearest Neighbors (KNN), Naïve Bayes (NB), Classification Tree (CT) and discriminative analysis (DA), are used. Simulation...
The use of technology has grown extensively in many fields, including that of the medical sciences. However, there has not been a lot of research done in the field regarding the use of basic stance parameters to classify the presence or lack thereof of locomotive disorders. In this paper, we shall be presenting the most optimum classification algorithm for the binary classification of a variety of...
Density estimation is a fundamental part of statistical analysis and data mining. In high-dimensional domains, parametric methods and the commonly used non-parametric methods like histograms or Kernel estimators fail to perform properly. In this paper, we present computationally efficient data structures for efficient implementation of the Bayesian sequential partitioning (BSP), as a framework for...
An improved batch process fault identification approach with kernel exponential discriminant analysis (KEDA) is proposed, in which performance index based on difference degree is given to identify fault classification. This method takes the advantages of both the kernel technology and the exponential discriminant analysis technique. The proposed KEDA method shows powerful ability in dealing with nonlinear,...
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