scholarly journals A High-Order Clustering Algorithm Based on Dropout Deep Learning for Heterogeneous Data in Cyber-Physical-Social Systems

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 11687-11693 ◽  
Author(s):  
Fanyu Bu
Keyword(s):  
Deep Learning ◽  
Clustering Algorithm ◽  
Social Systems ◽  
Heterogeneous Data ◽  
High Order

scholarly journals A High-Order CFS Algorithm for Clustering Big Data

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Fanyu Bu ◽  
Zhikui Chen ◽  
Peng Li ◽  
Tong Tang ◽  
Ying Zhang
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Clustering Algorithm ◽  
Big Data Analytics ◽  
Learning Model ◽  
Heterogeneous Data ◽  
High Order ◽  
Tensor Model ◽  
Industrial Internet ◽  
Deep Learning Model

With the development of Internet of Everything such as Internet of Things, Internet of People, and Industrial Internet, big data is being generated. Clustering is a widely used technique for big data analytics and mining. However, most of current algorithms are not effective to cluster heterogeneous data which is prevalent in big data. In this paper, we propose a high-order CFS algorithm (HOCFS) to cluster heterogeneous data by combining the CFS clustering algorithm and the dropout deep learning model, whose functionality rests on three pillars: (i) an adaptive dropout deep learning model to learn features from each type of data, (ii) a feature tensor model to capture the correlations of heterogeneous data, and (iii) a tensor distance-based high-order CFS algorithm to cluster heterogeneous data. Furthermore, we verify our proposed algorithm on different datasets, by comparison with other two clustering schemes, that is, HOPCM and CFS. Results confirm the effectiveness of the proposed algorithm in clustering heterogeneous data.

scholarly journals Predicting the Stock Market by Using the Clustering Algorithm in Big Data

2018 ◽  
Vol 7 (3.34) ◽  
pp. 68
Author(s):  
Veeramalai Sankaradass ◽  
T Praveen ◽  
S Padmavathy ◽  
R Bharathi
Keyword(s):  
Big Data ◽  
Stock Market ◽  
Clustering Algorithm ◽  
Heterogeneous Data ◽  
High Order ◽  
Huge Amount ◽  
Clustering Approach

As one of the essential approach in record mining and pattern popularity, the Possibilistic C-Means (PCM) algorithm has been widely utilized in evaluation and understanding discovery. It is highly difficult for PCM to provide an awesome end result for clustering huge amount of data particularly for heterogeneous data due to the fact that it is designed for smally established dataset. To address this trouble, we suggest a High-Order PCM (HOPCM). It is a set of rules for massive statistics clustering. The main aim of our proposed system is to find the profit or loss for the clients share based on clustering approach for the specific tickers. 

A new method to predict anomaly in brain network based on graph deep learning

2020 ◽  
Vol 31 (6) ◽  
pp. 681-689
Author(s):  
Jalal Mirakhorli ◽  
Hamidreza Amindavar ◽  
Mojgan Mirakhorli
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Brain Plasticity ◽  
Brain Network ◽  
High Order ◽  
Brain Diseases ◽  
Simultaneous Occurrence ◽  
Generative Adversarial Network ◽  
The Brain ◽  
Brain Connections

AbstractFunctional magnetic resonance imaging a neuroimaging technique which is used in brain disorders and dysfunction studies, has been improved in recent years by mapping the topology of the brain connections, named connectopic mapping. Based on the fact that healthy and unhealthy brain regions and functions differ slightly, studying the complex topology of the functional and structural networks in the human brain is too complicated considering the growth of evaluation measures. One of the applications of irregular graph deep learning is to analyze the human cognitive functions related to the gene expression and related distributed spatial patterns. Since a variety of brain solutions can be dynamically held in the neuronal networks of the brain with different activity patterns and functional connectivity, both node-centric and graph-centric tasks are involved in this application. In this study, we used an individual generative model and high order graph analysis for the region of interest recognition areas of the brain with abnormal connection during performing certain tasks and resting-state or decompose irregular observations. Accordingly, a high order framework of Variational Graph Autoencoder with a Gaussian distributer was proposed in the paper to analyze the functional data in brain imaging studies in which Generative Adversarial Network is employed for optimizing the latent space in the process of learning strong non-rigid graphs among large scale data. Furthermore, the possible modes of correlations were distinguished in abnormal brain connections. Our goal was to find the degree of correlation between the affected regions and their simultaneous occurrence over time. We can take advantage of this to diagnose brain diseases or show the ability of the nervous system to modify brain topology at all angles and brain plasticity according to input stimuli. In this study, we particularly focused on Alzheimer’s disease.

scholarly journals Interpretable deep learning for the remote characterisation of ambulation in multiple sclerosis using smartphones

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Andrew P. Creagh ◽  
Florian Lipsmeier ◽  
Michael Lindemann ◽  
Maarten De Vos
Keyword(s):  
Multiple Sclerosis ◽  
Deep Learning ◽  
Inertial Sensor ◽  
Heterogeneous Data ◽  
Fine Tuning ◽  
Sensor Data ◽  
Support Vector ◽  
Deep Convolutional Neural Networks ◽  
Healthcare Applications ◽  
Feature Based

AbstractThe emergence of digital technologies such as smartphones in healthcare applications have demonstrated the possibility of developing rich, continuous, and objective measures of multiple sclerosis (MS) disability that can be administered remotely and out-of-clinic. Deep Convolutional Neural Networks (DCNN) may capture a richer representation of healthy and MS-related ambulatory characteristics from the raw smartphone-based inertial sensor data than standard feature-based methodologies. To overcome the typical limitations associated with remotely generated health data, such as low subject numbers, sparsity, and heterogeneous data, a transfer learning (TL) model from similar large open-source datasets was proposed. Our TL framework leveraged the ambulatory information learned on human activity recognition (HAR) tasks collected from wearable smartphone sensor data. It was demonstrated that fine-tuning TL DCNN HAR models towards MS disease recognition tasks outperformed previous Support Vector Machine (SVM) feature-based methods, as well as DCNN models trained end-to-end, by upwards of 8–15%. A lack of transparency of “black-box” deep networks remains one of the largest stumbling blocks to the wider acceptance of deep learning for clinical applications. Ensuing work therefore aimed to visualise DCNN decisions attributed by relevance heatmaps using Layer-Wise Relevance Propagation (LRP). Through the LRP framework, the patterns captured from smartphone-based inertial sensor data that were reflective of those who are healthy versus people with MS (PwMS) could begin to be established and understood. Interpretations suggested that cadence-based measures, gait speed, and ambulation-related signal perturbations were distinct characteristics that distinguished MS disability from healthy participants. Robust and interpretable outcomes, generated from high-frequency out-of-clinic assessments, could greatly augment the current in-clinic assessment picture for PwMS, to inform better disease management techniques, and enable the development of better therapeutic interventions.

scholarly journals A Hybrid Deep Clustering Approach for Robust Cell Type Profiling Using Single-cell RNA-seq Data

2019 ◽  
Author(s):  
Suhas Srinivasan ◽  
Nathan T. Johnson ◽  
Dmitry Korkin
Keyword(s):  
Deep Learning ◽  
Single Cell ◽  
Clustering Algorithm ◽  
Hybrid Approach ◽  
Feature Learning ◽  
Specific Cell ◽  
Clustering Methods ◽  
Model Based Clustering ◽  
Clustering And Classification ◽  
Living Organisms

AbstractSingle-cell RNA sequencing (scRNA-seq) is a recent technology that enables fine-grained discovery of cellular subtypes and specific cell states. It routinely uses machine learning methods, such as feature learning, clustering, and classification, to assist in uncovering novel information from scRNA-seq data. However, current methods are not well suited to deal with the substantial amounts of noise that is created by the experiments or the variation that occurs due to differences in the cells of the same type. Here, we develop a new hybrid approach, Deep Unsupervised Single-cell Clustering (DUSC), that integrates feature generation based on a deep learning architecture with a model-based clustering algorithm, to find a compact and informative representation of the single-cell transcriptomic data generating robust clusters. We also include a technique to estimate an efficient number of latent features in the deep learning model. Our method outperforms both classical and state-of-the-art feature learning and clustering methods, approaching the accuracy of supervised learning. The method is freely available to the community and will hopefully facilitate our understanding of the cellular atlas of living organisms as well as provide the means to improve patient diagnostics and treatment.

Interval Prediction of Vehicle Journey Times with Heterogeneous Data Via a Deep Learning Framework

2022 ◽  
Author(s):  
Li Zhuang ◽  
Xinyue Wu ◽  
Andy H.F. Chow ◽  
Wei Ma ◽  
William Lam ◽  
...  
Keyword(s):  
Deep Learning ◽  
Heterogeneous Data ◽  
Learning Framework ◽  
Interval Prediction
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