DEEP LEARNING FOR EPILEPTIC SPIKE DETECTION

The algorithm is derived by considering the spike detection task as a blind equalization problem in a multiple-input, SEA Mode detection Sparse deflation If abortion criteria met MVDR ca[r]

The first part of the tutorial presents the basics of neural networks, neural word vectors, several simple models based on local windows and the math and algorithms of training via backp[r]

There is growing interest in utilizing artificial intelligence, and particularly deep learning, for computer vision in histopathology. While accumulating studies highlight expert-level performance of convolutional neural networks (CNNs) on focused classification tasks, most studies rely on probabili[r]

RNA sequencing technique (RNA-seq) enables scientists to develop novel data-driven methods for discovering more unidentified lincRNAs. Meantime, knowledge-based technologies are experiencing a potential revolution ignited by the new deep learning methods.

Each of the 542 samples is bill that was put to a vote. The votes are recorded as -1 for no and 1 for yes.
There are many missing values in this data set, corresponding to missed votes. Since our analysis depends on data values taken solely from { − 1 , 1 } , it was necessary to impu[r]

Accurately predicted contacts allow to compute the 3D structure of a protein. Since the solution space of native residue-residue contact pairs is very large, it is necessary to leverage information to identify relevant regions of the solution space, i.e. correct contacts.

Benefiting from big data, powerful computation and new algorithmic techniques, we have been witnessing the renaissance of deep learning, particularly the combination of natural language processing (NLP) and deep neural networks.

Due to the recent advances in deep learning, this model attracted researchers who have applied it to medical image analysis. However, pathological image analysis based on deep learning networks faces a number of challenges, such as the high resolution (gigapixel) of pathological images and the lack[r]

... agents, one needs to build models with deep architectures One class of such models is the class of deep neural networks Until recently, the idea to train deep neural networks had not shown much success... introduce a type of neural networks that has the potential for learning multiple layers of[r]

FPR is the percentage of normal instances incorrectly classified as anomaly, _5.1_ _RESULTS ON THE NAD-1998 DATA SET _ Here, we present the outcome of the k-Means, ID3 decision tree, k-m[r]

Porosity, permeability and water saturation of the reservoir zone were predicted by the RF analysis, compared with those obtained by the DL analysis and validated with the core measurements. It was found that there is a significant improvement in the analysis running time and the accuracy of the RF-[r]

Unsuccessful login attempts Connection attempts Successful logins from remote systems Attack prevention Same as for detection of attacks Same as for detection of attacks Detection of pol[r]

Statistical Machine Learning for High Dimensional Data Lecture 3Statistical Machine Learning for High Dimensional Data Lecture 3Statistical Machine Learning for High Dimensional Data Lecture 3Statistical Machine Learning for High Dimensional Data Lecture 3Statistical Machine Learning for High Dimens[r]

The constant progress in sequencing technology leads to ever increasing amounts of genomic data. In the light of current evidence transposable elements (TEs for short) are becoming useful tools for learning about the evolution of host genome. Therefore the software for genome-wide detection and anal[r]

Detection of highly divergent or yet unknown viruses from metagenomics sequencing datasets is a major bioinformatics challenge. When human samples are sequenced, a large proportion of assembled contigs are classified as “unknown”, as conventional methods find no similarity to known sequences.

One approach to improving the personalized treatment of cancer is to understand the cellular signaling transduction pathways that cause cancer at the level of the individual patient. In this study, we used unsupervised deep learning to learn the hierarchical structure within cancer gene expression d[r]

Cell counting from cell cultures is required in multiple biological and biomedical research applications. Especially, accurate brightfield-based cell counting methods are needed for cell growth analysis. With deep learning, cells can be detected with high accuracy, but manually annotated training da[r]

The potential encoding the compatibility between parts and locations is given by: TRANG 6 TABLE 1.Comparison of Different Discriminative Models Parts-Based Spatially Models Local Models L[r]

Molecular biomarkers that can predict drug efficacy in cancer patients are crucial components for the advancement of precision medicine. However, identifying these molecular biomarkers remains a laborious and challenging task.

The locations and shapes of synapses are important in reconstructing connectomes and analyzing synaptic plasticity. However, current synapse detection and segmentation methods are still not adequate for accurately acquiring the synaptic connectivity, and they cannot effectively alleviate the burden[r]