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This paper presents the design of a convolutional neural network architecture using the MatConvNet library for MATLAB in order to achieve the recognition of 2 classes of hand gestures: ”open” and ”closed”. Six architectures were implemented to which their hyperparameters and depth were varied to observe their behavior through the validation error in the training and accuracy in the estimation of each...
Sequential learning-based pattern classification aims at providing more accurate labeled maps by adding an extra step of classification using an augmented feature vector. In this paper, we evaluated the robustness of Optimum-Path Forest (OPF) classifier in the context of land-cover classification using both satellite and radar images, showing OPF can benefit from sequential learning theoretical basis.
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...
This work focuses on automatic prediction of the writer's biometrics including gender, handedness and age information. The proposed prediction system is based on the use of Histogram of Oriented Gradients (HOG), which aims to extract gradient directions from the handwritten text. The prediction task is achieved using SVM classifier. Experiments performed on IAM and KHATT datasets, reveal promising...
Since the beginning of the 21th century, with the continuous development of information technology, it has been the difficulty and hotpot in understanding field to study and analyze sports videos with computer technology. Because soccer videos have a broad mass background, it is more valuable to research on it. At present, there are many researches based on trajectory information of players or balls...
With the exponential increase of the data scale, the problem of feature selection has been the focus in statistical pattern recognition. In this paper, a new modified forward deep floating searching algorithm (SDFFS) is proposed to select a feature subset of d features from the original candidate-set of D features (d < D), which is an improvement of the state of the art SFFS algorithm. The SDFFS...
Scale-space corner detection (SSCD) has been drawing much attention in the past. Multi-scale corner detection (MSCD), which recognizes corners only at several scales, can be treated as a fast implementation of SSCD. In this paper, a new MSCD algorithm is proposed, which is based on an arithmetic mean (AM) of the k-cosine curvature values respectively computed at three scales. Compared to the existing...
Feature learning plays a crucial role in the successful human action recognition. There has been a number of approaches extracting action features from depth information and 3D skeletal data. However, either the skeleton information or the depth map is not accurate for feature learning unless complex descriptors are carefully designed and embedded. In this paper, we first propose a data sparsification...
A number of papers has presented a pattern recognition method for Parkinson's Disease (PD) detection. However, the literatures only able to classify subjects as either healthy of suffering from PD. This paper presents a pattern recognition method for multi stage classification of PD utilizing voice features. 22 features are obtained from University of California-Irvine (UCI) data repository. These...
This paper proposes a novel method for discriminating the supraventricular tachycardias and the ventricular tachycardias via a high dimensional linear discriminant function and a perceptron with a multi-piece domain activation function having multi-level functional values. The algorithm is implemented via the mobile application. First, the discrete cosine transform is applied to each training electrocardiogram...
The recognition of forged fingerprints at crime scenes is a very old challenge. Various forgery techniques can be applied to produce such traces. However, the detection of such forgeries usually involves a thorough manual inspection. For the example of fingerprints printed using ink-jet printers and artificial sweat, first pattern recognition approaches are proposed in prior work employing two different...
Activity recognition is an important subject with many applications in health care, emergency care, and assisted living. Nowadays, activity information can be acquired using small accelerometers connected to the body, including the ones available in smartphones. In this study, we assessed the influence of autoregressive model parameters or features on activity detection or classification. Our results...
We propose Magemite, a fine-grained input system that exploits the around device space (ADS) as an expansion of the limited input area. The key insight underlying Magemite is, magnetic sensor integrated in smart devices can sense nearby magnetic field strength. Using a permanent magnet, users could “write” in ADS to communicate with matched devices. Different from previous magnetic-sensing schemes...
The different ways of describing the characteristics of the image. The application of autoencoder for image classification. The results of experiments showing the effectiveness of autoencoder for solving pattern classification.
To alleviate the loads of tracking web log file by human effort, machine learning methods are now commonly used to analyze log data and to identify the pattern of malicious activities. Traditional kernel based techniques, like the neural network and the support vector machine (SVM), typically can deliver higher prediction accuracy. However, the user of a kernel based techniques normally cannot get...
Automatic classification of tropical wood species is becoming more important especially for timber exporting countries due to the considerable economic challenge as a result of fraudulent labelling of timber species at the custom checkpoints. Hence, a reliable automated wood species recognition system is needed to inspect the wood species labelling at the checkpoints. A tropical wood species classification...
Today in data mining research we are daily confronted with large amount of data. Most of the time, these data contain redundant and irrelevant data that it is important to extract before a learning task in order to get good accuracy. The fact that today's computers are more powerful does not solves the problems of this ever-growing data. It is therefore crucial to find techniques which allow handling...
During the last decade, the extraction and reuse of building blocks of knowledge for the learning process of Extended Classifier System (XCS) in Multiplexer (MUX) problem domain have been demonstrate feasible by using Code Fragment (CF) (i.e. a tree-based structure ordinarily used in the field of Genetic Programming (GP)) as the representation of classifier conditions (the resulting system was called...
In imbalanced learning, most standard classification algorithms usually fail to properly represent data distribution and provide unfavorable classification performance. More specifically, the decision rule of minority class is usually weaker than majority class, leading to many misclassification of expensive minority class data. Motivated by our previous work ADASYN [1], this paper presents a novel...
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