Titlebook: Brain and Health Informatics; International Confer Kazayuki Imamura,Shiro Usui,Ning Zhong Conference proceedings 2013 Springer Internationa

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Morgane Joly,éric Renault,Fabian Rivièreconnectivity of human brains, investigating sex differences across male and female connectomes (brain-graphs) for the knowledge discovery problem “.”. One of our main findings discloses the statistical difference at the pars orbitalis of the connectome between sexes, which has been shown to function

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Machine Learning for Networkingrom measurements. This paper addresses the measurement design problem of selecting . feasible measurements such that the mutual information between the unknown image and measurements is maximized, where . is a given budget. To calculate the mutual information, we utilize correlations of adjacent fun

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Afsane Zahmatkesh,Chung-Horng Lungedures usually generate high-dimensional data. This complicates statistical analysis and modeling, resulting in high computational complexity and typically more complicated models. This study uses the features extracted from Positron Emission Tomography imagery by 3D Stereotactic Surface Projection.

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Lecture Notes in Computer Science extracting not only parts that are exactly the same but also similar parts having a few differences since time-series data of cerebral blood flow is reported to be affected by various factors, and real data may therefore differ from a model system. To confirm the effectiveness of the proposed algor

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https://doi.org/10.1007/978-1-4842-7032-5e obtaining the rational result in a . fashion is more important in many real-world problems. A typical example in medical/brain informatics is that big stream data and protraction of analysis time are causing a burden of medical doctors or patients and therefore the feature extraction in a real-tim

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Oracle Machine Learning for SQL,s paper proposes an unsupervised multi-scale K-means (MSK-means) algorithm to distinguish epileptic EEG signals from normal EEGs. The random initialization of the K-means algorithm can lead to wrong clusters. Based on the characteristics of EEGs, the MSK-means algorithm initializes the coarse-scale

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