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In this work we develop and demonstrate a probabilistic generative model for phytoplankton communities. The proposed model takes counts of a set of phytoplankton taxa in a timeseries as its training data, and models communities by learning sparse co-occurrence structure between the taxa. Our model is probabilistic, where communities are represented by probability distributions over the species, and...
Account sharing is a significant problem for online recommender systems to generate accurate personalized recommendations. To solve this problem, one not only has to identify whether an account is shared, but also needs to recognize the different users sharing that account. However, to generate relevant, personalized recommendations, the particular user under a shared account has to be correctly identified...
Security is one of the top concerns of any enterprise. Most security practitioners in enterprises rely on correlation rules to detect potential threats. While the rules are intuitive to design, each rule is independently defined per log source, unable to collectively address heterogeneity of data from a myriad of enterprise networking and security logs. Furthermore, correlation rules do not look for...
Hand-shape recognition is an important problem in computer vision with significant societal impact. In this work, we introduce a new image dataset for Irish Sign Language (ISL) recognition and we compare between two recognition approaches. The dataset was collected by filming human subjects performing ISL hand-shapes and movements. Then, we extracted frames from the videos. This produced a total of...
Distance or similarity measures are essence components used by distance-based recognition techniques. Since the Euclidean distance function is the most widely used distance metric in PCA and LDA recognition systems , no empirical study examines the recognition performance based on these two methods by using different distance functions, especially for biometric authentication domain problems. The...
Deep learning based hyperspectral image (HSI) classification have recently shown promising performance. However, complex network architecture, tedious training process and effective utilization of spatial/contextual information in deep network limits the application and performance of deep learning. In this paper, for an effective spectral-spatial feature extraction , an improved deep network, spatial...
Countering network threats, particularly intrusions, is a challenging area of research in the field of information security. Intruders use sophisticated mechanisms to hide the attack payload and break the detection techniques. To overcome that, various unsupervised learning approaches from the field of machine learning and pattern recognition have been employed. The most popularly used method is Principal...
In order to improve the accuracy and stability of industrial fault detection and diagnosis, this paper introduces the deep learning theory and proposes an improved Deep Belief Networks (DBNs). In the first, this paper introduces the “centering trick” in the pre-training process of network. This method is done by subtracting offset values from visible and hidden variables. Then, in the process of network...
K-Nearest Neighbor (KNN) is a commonly used fault diagnosis method, which is based on Euclidean distance between samples to carry out fault diagnosis. The differences between the variables have a direct effect on the Euclidean distance, which affects the KNN fault diagnosis effect. After the dimensional normalization, there are also some problems such as the decrease of variable diversity, and the...
To realize Electrocardiography (ECG) signals monitoring systems, compressive sensing (CS) is a new technique to reduce power of biosensors and data transmission. Instead of spending high complexity on reconstructing back to data domain to do signal analysis, compressed analysis (CA) exploits the data structure preserved by CS to directly analyze in the compressed domain. However, compressively-sensed...
Video surveillance systems have enabled the monitoring of complex events in several places, such as airports, banks, streets, schools, industries, among others. Due to the massive amount of multimedia data acquired by video cameras, traditional visual inspection by human operators is a very tedious and time consuming task, whose performance is affected by fatigue and stress. A challenge is to develop...
Active shape model is widely used for facial feature localization. Regarding the traditional ASM algorithm can't describe the object shape precisely, an improved ASM algorithm is proposed. At first, we establish shape model and use PCA (Principle Component Analysis) to transform high-dimensional data to lower dimensions. Another work is to establish local texture model giving sample points with different...
The death of the patients is an important event in the intensive care unit (ICU), mortality risk prediction thus offers much information for clinical decision making. However, Patient ICU mortality prediction faces challenges in many aspects, such as high dimensionality, imbalance distribution. In this paper, we modified the cost-sensitive principal component analysis (CSPCA), which is denoted by...
Subspace learning is one of the most foundational tasks in computer vision with applications ranging from dimensionality reduction to data denoising. As geometric objects, subspaces have also been successfully used for efficiently representing certain types of invariant data. However, methods for subspace learning from subspace-valued data have been notably absent due to incompatibilities with standard...
A Brain-Computer Interface (BCI) speller system based on the Steady-State Visually Evoked Potentials (SSVEP) paradigm is presented. The potentials are elicited through the gaze fixation at one out of the four checkerboards shown on screen, which are flickering at 5, 12, 15 and 20 Hz. After the feature extraction, two dimensionality reduction algorithms, Principal Components Analysis (PCA) and Linear...
With millions of people suffering from dementia worldwide, the global prevalence of dementia has a significant impact on the patients' lives, their caregivers' physical and emotional states, and the global economy. Early diagnosis of dementia helps in finding suitable therapies that reduce or even prevent further deterioration of patients' cognitive abilities. MRI scans are shown to be the most effective...
For modern industrial processes, timely detection of incipient faults is of vital importance so as to ensure safe and optimal process operation. Though recently statistical process monitoring (SPM) has been extensively studied and widely applied in practice, conventional multivariate statistics are usually not sensitive to incipient faults. In this paper, a new multivariate statistical index called...
With the extensive application of machine learning algorithms in bioinformatics, more and more computer researchers are beginning to focus on this field. Polyadenylation of messenger RNA (mRNA) is one of the key steps of gene expression in eukaryotes, polyadenylation site marks the end of transcription, it is of great significance to explore prediction of the site of gene sequences encoding gene....
Biometrics is an active research field that is increasingly being integrated into current technology. As a result, more and more people are becoming familiar with biometric technics such as fingerprint or facial recognition. Nevertheless, there are innovative techniques such as ear-based biometrics which are not very well known yet because they are at initial stages of research. In this work, an ear...
We propose a method that uses kernel method-based algorithms to implement an autoencoder. Deep learning-based algorithms have two characteristics, one is the high level data abstraction, the other is the multiple level data transformations and representations. The kernel method is one of the approaches that can be used in linear and non-linear transformations. It should be one of the implementations...
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