Decoding human activity accurately from wearable sensors can aid in applications related to healthcare and context awareness. The present approaches this domain use recurrent and/or convolutional models capture the spatio-temporal features time-series data multiple sensors. We propose a deep neural network architecture that not only captures of sensor but also selects, learns important time points by utilizing self-attention mechanism. show validity proposed approach across different...

Successive layers in convolutional neural networks (CNN) extract different features from input images. Applications of CNNs to detect abnormalities the 2D images or 3D volumes body organs have recently become popular. However, computer-aided detection diseases using deep CNN is challenging due absence a large set training medical images/scans and relatively small hard abnormalities. In this paper, we propose method for normalizing volumetric scans intensity profile samples. This aids by...

Betweenness centrality is widely used as a measure, with applications across several disciplines. It measure that quantifies the importance of vertex based on vertex's occurrence shortest paths in graph. This global and order to find betweenness node, one supposed have complete information about Most algorithms are assume constancy graph not efficient for _dynamic networks_. We propose technique update when nodes added or deleted. Observed experimentally, real graphs, our algorithm speeds up...

Specialized processing in the brain is performed by multiple groups of regions organized as functional modules. Although, vivo studies modules involve Magnetic Resonance Imaging (fMRI) scans, methods used to derive from networks ignore individual differences architecture and use incomplete connectivity information. To correct this, we propose an Iterative Consensus Spectral Clustering (ICSC) algorithm that detects most representative dense weighted matrices derived scans. The ICSC derives...

In this paper, a brief overview of real time implementation next generation Robust, Tracking, Disturbance rejecting, Aggressive (RTDA) controller and Model Predictive Control (MPC) is provided. The control algorithm implemented through MATLAB. plant model used in design obtained using system identification tool integral response method. developed Simulink jMPC tool, which will be executed time. outputs are tested for various constraint values to obtain the desirable results. Hardware Loop...

Functional modules in the human brain support its drive for specialization whereas hubs act as focal points information integration. Brain are regions that have a large number of both within and between module connections. We argue weak connections functional networks lead to misclassification hubs. In order resolve this, we propose new measure called ambivert degree considers node’s well connection weights identify nodes with high Using resting-state MRI scans from Human Connectome Project,...

Heterogeneity is present in Alzheimer's disease (AD), making it challenging to study. To address this, we propose a graph neural network (GNN) approach identify subtypes from magnetic resonance imaging (MRI) and functional MRI (fMRI) scans. Subtypes are identified by encoding the patients' scans brain graphs (via cortical similarity networks) clustering representations learnt GNN. These subtyping information used construct population for an ensemble of local networks, each producing...

Abstract Functional magnetic resonance imaging (fMRI) is used to capture complex and dynamic interactions between brain regions while performing tasks. Task related alterations in the have been classified as task specific general, depending on whether they are particular a or common across multiple Using recent attempts interpreting deep learning models, we propose an approach determine both general architectures of functional brain. We demonstrate our methods with reference‐based decoder...

Functional connectivity of the human brain and hierarchical modular architecture functional networks can be investigated using magnetic resonance imaging (fMRI). Various network models, such as power-law have been explored before to study networks. In order investigate plausibility modeling with models based on distribution node degree connection weights, we will compute goodness-of-fit several resting-state fMRI scans gathered in Human Connectome Project. Our experiments suggest that...

Abstract Deep neural networks have been demonstrated to extract high level features from neuroimaging data when classifying brain states. Identifying salient characterizing states further refines the focus of clinicians and allows design better diagnostic systems. We demonstrate this while performing classification resting-state functional magnetic resonance imaging (fMRI) scans patients suffering Alzheimer’s Disease (AD) Mild Cognitive Impairment (MCI), Cognitively Normal (CN) subjects...

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective:</i> Brain state classification by applying deep learning techniques on neuroimaging data has become a recent topic of research. However, unlike domains where the is low dimensional or there are large number available training samples, high and few samples. To tackle these issues, we present sparse feedforward neural architecture for encoding decoding structural connectome human brain....

Motivation: Accurate quantitative information about the protein abundance is crucial for understanding a biological system and its dynamics. Protein commonly estimated using label-free, bottom-up mass spectrometry protocols. Here, proteins are digested into peptides before quantification via spectrometry. However, missing peptide values, which can make up more than 50% of all common issue. They result in then hinder accurate reliable downstream analyses. Results: To impute we propose...

Accurate quantitative information about protein abundance is crucial for understanding a biological system and its dynamics. Protein commonly estimated using label-free, bottom-up mass spectrometry (MS) protocols. Here, proteins are digested into peptides before quantification via MS. However, missing peptide values, which can make up more than 50% of all common issue. They result in then hinder accurate reliable downstream analyses.

Bone marrow examination has become increasingly important for the diagnosis and treatment of hematologic other illnesses. The present methods analyzing bone biopsy samples involve subjective inaccurate assessments by visual estimation pathologists. Thus, there is a need to develop automated tools assist in analysis samples. However, lack publicly available standardized high-quality datasets that can aid research development provide consistent objective measurements. In this paper, we...

Bone marrow reticulin fibrosis is associated with varied benign as well malignant hematological conditions. The assessment of important in the diagnosis, prognostication and management such disorders. current methods for quantification are inefficient prone to errors. Therefore, there a need automated tools accurate consistent reticulin. However, lack standardized datasets has hindered development tools. In this study, we present comprehensive dataset that comprises 201 Marrow Biopsy images...

Resting-state brain functional networks (BFNs) give information about spatio-temporal interactions in the when subject is at rest or not performing any task. Investigations with resting-state Magnetic Resonance Imaging (rs-fMRI) data have shown that BFN possesses a modular architecture. However, nodal degree distributions within modules of BFN, over large population subjects, thus far been investigated. The aim this work to assign memberships regions and study modules. We fit different...

Abstract Neuroscientific knowledge points to the presence of redundancy in correlations brain’s functional activity. These redundancies can be removed mitigate problem overfitting when deep neural network (DNN) models are used classify neuroimaging datasets. We propose an algorithm that removes insignificant nodes DNNs a layerwise manner and then adds subset correlated features single shot. When performing experiments with MRI datasets for classifying patients from healthy controls, we were...