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Building recognition from images is a challenging task since pictures can be taken from different angles and under different illumination conditions. Most of the building recognition methods use local and global handcrafted image features and do not consider the rejection scenario, where the method have to be capable of identifying if a given image does not belong to any of the classes of interest...
Many noninvasive continuous blood pressure measurements using photoplethysmography (PPG) are still inadequate in terms of accuracy and stability, which hinders the practical application of this method. This paper proposes a model based on ensemble method for BP estimation using PPG. A number of blood pressure calculation base-models is built on the same training data. These base-models are used to...
Face detection in unconstrained environments is a challenging problem due to partial occlusions with pose variations. Existing partial occluded face detection methods require training several models, computing hand-crafted features, or both. In this paper, our contributions are two-fold. First, we propose our Large-Scale Deep Learning (LSDL), a method that requires a single Convolutional Neural Network...
Sentiment analysis, also known as opinion mining, seeks to figure out points of view from documents. Sentiment classification is a specific task of sentiment analysis that divides documents into positive and negative sentiment polarities according to the attitudes expressed. Feature extraction is a significant part of sentiment classification. Traditional feature extraction methods mine statistical...
Nowadays' road network infrastructure failing to cope up with the exponential increase in vehicular population, there is a constant strive to find smarter ways to deal with it using existing infrastructure. Intelligent Transport System is at the forefront of this, one of the aims is accurate and sophisticated traffic predictions that ensure smooth and hassle free commuting and administrative experience...
This paper introduces a novel method, based on Gaussian Markov Random Field Model with back-propagation learning algorithm to retrieve multi-spectral satellite color imagery. The proposed method segregates the texture part and structure part of the imagery, and extracts features in the texture and structure parts separately. The extracted features are formed as a feature vector. The feature vector...
Dermatological diseases are the most prevalent diseases worldwide. Despite being common, its diagnosis is extremely difficult and requires extensive experience in the domain. In this research paper, we provide an approach to detect various kinds of these diseases. We use a dual stage approach which effectively combines Computer Vision and Machine Learning on clinically evaluated histopathological...
Saliency models provide heatmaps highlighting the probability of each pixel to attract human gaze. To define image's important regions, features maps are extracted. The rarity, surprise or contrast are computed leading to conspicuity maps, showing important regions of each feature map. The final saliency map is obtained by merging these maps. The fusion process is usually a linear combination of the...
Artificial Intelligence or AI is a subfield of computer science, which can be defined as the intelligence exhibited by a machine or a software having a remarkable impact on the field of biology and medicine. Imaging, on the other hand has become an essential component of many fields in medicine, biomedical applications, biotechnology and laboratory research by which images are processed and analysed...
Spoken keyword recognition has been under the spotlight for the past several decades, but has gained significant attention in recent years due to the rapid increase in front-end technology applications for mobile and wearable computing. This work presents the trade-off in performance between Artificial Neural Networks (ANN) and Dynamic Time Warping (DTW) methodologies, implemented for this task under...
Foreign Exchange (Forex) market is a complex and challenging task for prediction due to uncertainty movement of exchange rate. However, these movements over timeframe also known as historical Forex data that offered a generic repeated trend patterns. This paper uses the features extracted from trend patterns to model and predict the next day trend. Hidden Markov Models (HMMs) is applied to learn the...
Multi-label image annotation has received significant attention in the research community over the past few years. Multi-label automatic image annotation assigns keywords to the image based on low level features automatically. In this paper, we present an extensive survey on the research work carried out in the area of multi-label image annotation by using statistical and machine learning approaches...
This research investigated the possibility of side view human gait silhouette to be used for recognition of walking and running gait based on model-based approach. Markerless model with model based is used to produce the vertical angles of both hip and knee with respect to thigh for 32 image sequences as feature vectors for both legs for one complete cycle sequences. Overall, a total of 128 features...
The newborn's skull is composed of already ossified parts of the flat bone connected by areas of fibrous membrane not yet ossified, which are called fontanels. At birth, an infant has six of such fontanels. These two different tissue types forming the outer part of the neuro-cranium have different electrical conductivities. Thus, it is important to determine the exact geometry of the fontanels if...
The BP Deepwater Horizon oil spill disaster has reminded us, once again, the potential for systemic failures in complex engineered systems. But such systemic failures are not limited to the chemical and petrochemical industries alone. The 2003 Northeast electrical power blackout was a systemic failure. Financial disasters such as Enron, WorldCom, subprime derivatives market, and so on, also belong...
This study reviews the features used in the previous Automated Essay Scoring (AES) system, and attempts to develop a new linguistic feature-thematic feature for AES systems. According to Functional Grammar, theme is the point of departure for message, the element with which the clause is concerned. The thematic structure is an important method to promote essay coherence, and to present the message...
In this paper, we introduce an Intrusion Detection system (IDS) based Hybrid Evolutionary Neural Network (HENN). A brief overview of IDS, genetic algorithm, and related detection techniques are discussed. The system architecture is also introduced. Factors affecting the genetic algorithm are addressed in detail. Unlike other implementations of IDS, Input features, network structure and connection...
In this paper, we present a model based on the Neural Network (NN) for classifying Arabic texts. We propose the use of Singular Value Decomposition (SVD) as a preprocessor of NN with the aim of further reducing data in terms of both size and dimensionality. Indeed, the use of SVD makes data more amenable to classification and the convergence training process faster. Specifically, the effectiveness...
In this paper, an efficient heart beat classification algorithm suitable for implementation on mobile devices is presented. A simplified ECG model is used for feature extraction in the time domain. The QRS complex is modeled using straight lines, while P and T waves are modeled using parabolas. The model parameters are estimated by minimizing the root mean square (RMS) of the model error. Heart beats...
Measuring similarity has a wide range of application in information retrieval, machine translation or other related fields. In this paper, we proposed a text similarity computation based on improved optimal assignment model, which combine the improved Hungarian algorithm with the semantic similarity of terms to obtain the maximum semantic similarity between two documents or between a query and a document...
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