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REMOTE INTELLIGENT MEDICAL MONITORING DATA TRANSMISSION NETWORK OPTIMIZATION BASED ON DEEP LEARNING

SCALABLE COMPUTING-PRACTICE AND EXPERIENCE 2024年第3期25卷 1748-1756页

作者： Wang, Run Zhengzhou Univ Affiliated Hosp 5 Zhengzhou 450052 Peoples R China

A hospital operating status evaluation data analysis system was established based on the autoencoder's network. The Gibbs sampling method is used to obtain the approximate distribution of RBM. In addition, the autoencoder neural network can also select feature dimensions that can better characterize the characteristics of financial operation data from a large amount of financial operation data. Deep learning methods are used to study the redundant information elimination method and the generation mechanism of multi -source heterogeneity in multi -source heterogeneous networks. The principle of intrinsic compression is used to reduce the dimensionality of the redundancy in the network and obtain the compression redundancy objective function. This article sets thresholds for information classification on the Internet. The approach was tested using financial data from a medical institution. Use smart encoders to extract 17 financial indicators from financial data that can be used for modeling. The evaluation results are used as the output vector of the model. Comparative experiments show that the AUC value and accuracy of the method proposed in this article can be improved by 0.84 and 83.33% compared with the AUC value of shallow logistic regression and BP neural network. This algorithm has apparent improvements.

关键词： Deep learning DBN RBM autoencoder Network data Redundant information Optimization elimination Medical data mining

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Deep Compressed Sensing for Learning Submodular Functions

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SENSORS 2020年第9期20卷 2591-2591页

作者： Tsai, Yu-Chung Tseng, Kuo-Shih Natl Cent Univ Dept Math Taoyuan 32001 Taiwan

The AI community has been paying attention to submodular functions due to their various applications (e.g., target search and 3D mapping). Learning submodular functions is a challenge since the number of a function's outcomes of N sets is 2N. The state-of-the-art approach is based on compressed sensing techniques, which are to learn submodular functions in the Fourier domain and then recover the submodular functions in the spatial domain. However, the number of Fourier bases is relevant to the number of sets' sensing overlapping. To overcome this issue, this research proposed a submodular deep compressed sensing (SDCS) approach to learning submodular functions. The algorithm consists of learning autoencoder networks and Fourier coefficients. The learned networks can be applied to predict 2N values of submodular functions. Experiments conducted with this approach demonstrate that the algorithm is more efficient than the benchmark approach.

关键词： submodularity compressed sensing autoencoder deep learning

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Sparse Feature Learning of Hyperspectral Imagery via Multiobjective-Based Extreme Learning Machine

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SENSORS 2020年第5期20卷 1262-1262页

作者： Fang, Xiaoping Cai, Yaoming Cai, Zhihua Jiang, Xinwei Chen, Zhikun China Univ Geosci Dept Comp Sci Wuhan 430074 Peoples R China Beibu Gulf Univ Beibu Gulf Big Data Resources Utilizat Lab Qinzhou 535011 Peoples R China Beibu Gulf Univ Guangxi Key Lab Marine Disaster Beibu Gulf Qinzhou 535011 Peoples R China

Hyperspectral image (HSI) consists of hundreds of narrow spectral band components with rich spectral and spatial information. Extreme Learning Machine (ELM) has been widely used for HSI analysis. However, the classical ELM is difficult to use for sparse feature leaning due to its randomly generated hidden layer. In this paper, we propose a novel unsupervised sparse feature learning approach, called Evolutionary Multiobjective-based ELM (EMO-ELM), and apply it to HSI feature extraction. Specifically, we represent the task of constructing the ELM autoencoder (ELM-AE) as a multiobjective optimization problem that takes the sparsity of hidden layer outputs and the reconstruction error as two conflicting objectives. Then, we adopt an Evolutionary Multiobjective Optimization (EMO) method to solve the two objectives, simultaneously. To find the best solution from the Pareto solution set and construct the best trade-off feature extractor, a curvature-based method is proposed to focus on the knee area of the Pareto solutions. Benefited from the EMO, the proposed EMO-ELM is less prone to fall into a local minimum and has fewer trainable parameters than gradient-based AEs. Experiments on two real HSIs demonstrate that the features learned by EMO-ELM not only preserve better sparsity but also achieve superior separability than many existing feature learning methods.

关键词： extreme learning machine autoencoder autoencoder evolutionary multiobjective optimization sparse feature learning hyperspectral imagery

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Identifying subgroups of eating behavior traits unrelated to obesity using functional connectivity and feature representation learning

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HUMAN BRAIN MAPPING 2024年第1期45卷 e26581-e26581页

作者： Choi, Hyoungshin Byeon, Kyoungseob Lee, Jong-eun Hong, Seok-Jun Park, Bo-yong Park, Hyunjin Sungkyunkwan Univ Dept Elect & Comp Engn Suwon South Korea Inst Basic Sci Ctr Neurosci Imaging Res Suwon South Korea Child Mind Inst Ctr Developing Brain New York NY USA Sungkyunkwan Univ Dept Biomed Engn Suwon South Korea Inha Univ Dept Data Sci Incheon South Korea Inha Univ Dept Stat & Data Sci Incheon South Korea Sungkyunkwan Univ Sch Elect & Elect Engn Suwon South Korea

Eating behavior is highly heterogeneous across individuals and cannot be fully explained using only the degree of obesity. We utilized unsupervised machine learning and functional connectivity measures to explore the heterogeneity of eating behaviors measured by a self-assessment instrument using 424 healthy adults (mean +/- standard deviation [SD] age = 47.07 +/- 18.89 years;67% female). We generated low-dimensional representations of functional connectivity using resting-state functional magnetic resonance imaging and estimated latent features using the feature representation capabilities of an autoencoder by nonlinearly compressing the functional connectivity information. The clustering approaches applied to latent features identified three distinct subgroups. The subgroups exhibited different levels of hunger traits, while their body mass indices were comparable. The results were replicated in an independent dataset consisting of 212 participants (mean +/- SD age = 38.97 +/- 19.80 years;35% female). The model interpretation technique of integrated gradients revealed that the between-group differences in the integrated gradient maps were associated with functional reorganization in heteromodal association and limbic cortices and reward-related subcortical structures such as the accumbens, amygdala, and caudate. The cognitive decoding analysis revealed that these systems are associated with reward- and emotion-related systems. Our findings provide insights into the macroscopic brain organization of eating behavior-related subgroups independent of obesity. We systematically investigated the heterogeneity of eating behavior traits of healthy adults using unsupervised machine learning and functional connectivity. We identified three distinct subgroups showing different eating behavior traits independent of the degree of ***

关键词： autoencoder eating behavior functional connectivity integrated gradient manifold learning representation learning subgroup

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Dimensionality reduction for images of IoT using machine learning

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SCIENTIFIC REPORTS 2024年第1期14卷 1-13页

作者： Ali, Ibrahim Wassif, Khaled Bayomi, Hanaa Cairo Univ Fac Comp & Artificial Intelligence Comp Sci Dept Giza Egypt

Sensors, wearables, mobile devices, and other Internet of Things (IoT) devices are becoming increasingly integrated into all aspects of our lives. They are capable of gathering enormous amounts of data, such as image data, which can then be sent to the cloud for processing. However, this results in an increase in network traffic and latency. To overcome these difficulties, edge computing has been proposed as a paradigm for computing that brings processing closer to the location where data is produced. This paper explores the merging of cloud and edge computing for IoT and investigates approaches using machine learning for dimensionality reduction of images on the edge, employing the autoencoder deep learning-based approach and principal component analysis (PCA). The encoded data is then sent to the cloud server, where it is used directly for any machine learning task without significantly impacting the accuracy of the data processed in the cloud. The proposed approach has been evaluated on an object detection task using a set of 4000 images randomly chosen from three datasets: COCO, human detection, and HDA datasets. Results show that a 77% reduction in data did not have a significant impact on the object detection task's accuracy.

关键词： Edge computing Deep learning IoT autoencoder

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Multi-omics integration for neuroblastoma clinical endpoint prediction

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BIOLOGY DIRECT 2018年第20180427期13卷 5-5页

作者： Francescatto, Margherita Chierici, Marco Dezfooli, Setareh Rezvan Zandona, Alessandro Jurman, Giuseppe Furlanello, Cesare Fdn Bruno Kessler Via Sommar 18 I-38123 Trento Italy Univ Trento Ctr Integrat Biol Via Sommar 9 I-38123 Trento Italy Univ Padua Dept Informat Engn Padua Italy

Background: High-throughput methodologies such as microarrays and next-generation sequencing are routinely used in cancer research, generating complex data at different omics layers. The effective integration of omics data could provide a broader insight into the mechanisms of cancer biology, helping researchers and clinicians to develop personalized therapies. Results: In the context of CAMDA 2017 Neuroblastoma Data Integration challenge, we explore the use of Integrative Network Fusion (INF), a bioinformatics framework combining a similarity network fusion with machine learning for the integration of multiple omics data. We apply the INF framework for the prediction of neuroblastoma patient outcome, integrating RNA-Seq, microarray and array comparative genomic hybridization data. We additionally explore the use of autoencoders as a method to integrate microarray expression and copy number data. Conclusions: The INF method is effective for the integration of multiple data sources providing compact feature signatures for patient classification with performances comparable to other methods. Latent space representation of the integrated data provided by the autoencoder approach gives promising results, both by improving classification on survival endpoints and by providing means to discover two groups of patients characterized by distinct overall survival (OS) curves.

关键词： Neuroblastoma Integration Prediction Classification autoencoder

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Early prediction of coronavirus disease epidemic severity in the contiguous United States based on deep learning

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RESULTS IN PHYSICS 2021年 25卷 104287-104287页

作者： Kao, I-Hsi Perng, Jau-Woei Natl Sun Yat Sen Univ Dept Mech & Electromech Engn Kaohsiung 804 Taiwan

In November 2019, the coronavirus disease outbreak began, caused by the novel severe acute respiratory syndrome coronavirus 2. In just over two months, the unprecedented rapid spread resulted in more than 10,000 confirmed cases worldwide. This study predicted the infectious spread of coronavirus disease in the contiguous United States using a convolutional autoencoder with long short-term memory and compared its predictive performance with that of the convolutional autoencoder without long short-term memory. The epidemic data were obtained from the World Health Organization and the US Centers for Disease Control and Prevention from January 1st to April 6th, 2020. We used data from the first 366,607 confirmed cases in the United States. In this study, the data from the Centers for Disease Control and Prevention were gridded by latitude and longitude and the grids were categorized into six epidemic levels based on the number of confirmed cases. The input of the convolutional autoencoder with long short-term memory was the distribution of confirmed cases 14 days before, whereas the output was the distribution of confirmed cases 7 days after the date of testing. The mean square error in this model was 1.664, the peak signal-to-noise ratio was 55.699, and the structural similarity index was 0.99, which were better than those of the corresponding results of the convolutional autoencoder. These results showed that the convolutional autoencoder with long short-term memory effectively and reliably predicted the spread of infectious disease in the contiguous United States.

关键词： autoencoder Convolutional neural networks Severe acute respiratory syndrome coronavirus 2 Coronavirus disease Deep learning Long short-term memory Machine learning Prediction Transmission Epidemic

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Chaos Detection and Mitigation in Swarm of Drones using Machine Learning Techniques and Chaotic Attractors

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2022年第6期13卷 449-460页

作者： Nebe, Emmanuel Sanni, Mistura Laide Adetona, Rasheed Ayodeji Akinyemi, Bodunde Odunola Bello, Sururah Apinke Aderounmu, Ganiyu Adesola Obafemi Awolowo Univ Dept Comp Sci & Engn Ife Nigeria Obafemi Awolowo Univ Dept Math Ife Nigeria

Mostvexisting identification and tackling of chaos in swarm drone missions focus on single drone scenarios. There is a need to assess the status of a system with multiple drones, hence, this research presents an on-the-fly chaotic behavior detection model for large numbers of flying drones using machine learning techniques. A succession of three Artificial Intelligence knowledge discovery procedures, Logistic Regression (LR), Convolutional Neural Network (CNN), Gaussian Mixture Models (GMMs) and Expectation-Maximization (EM) were employed to reduce the dimension of the actual data of the swarm of drone's flight and classify it as non-chaotic and chaotic. A one-dimensional, multi-layer perceptive, deep neural network-based classification system was also used to collect the related characteristics and distinguish between chaotic and non-chaotic conditions. The Rossler system was then employed to deal with such chaotic conditions. Validation of the proposed chaotic detection and mitigation technique was performed using real-world flight test data, demonstrating its viability for real-time implementation. The results demonstrated that swarm mobility horizon-based monitoring is a viable solution for real-time monitoring of a system's chaos with a significantly reduced commotion effect. The proposed technique has been tested to improve the performance of fully autonomous drone swarm flights.

关键词： Chaos detection swarm of drones machine learning autoencoder Rossler system

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A Multi-Site Anti-Interference Neural Network for ASD Classification

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ALGORITHMS 2023年第7期16卷 315-315页

作者： Lv, Wentao Li, Fan Luo, Shijie Xiang, Jie Taiyuan Univ Technol Coll Informat & Comp Taiyuan 030600 Peoples R China

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that can reduce quality of life and burden families. However, there is a lack of objectivity in clinical diagnosis, so it is very important to develop a method for early and accurate diagnosis. Multi-site data increases sample size and statistical power, which is convenient for training deep learning models. However, heterogeneity between sites will affect ASD recognition. To solve this problem, we propose a multi-site anti-interference neural network for ASD classification. The resting state brain functional image data provided by the multi-site is used to train the ASD classification model. The model consists of three modules. First, the site feature extraction module is used to quantify the inter-site heterogeneity, in which the autoencoder is used to reduce the feature dimension. Secondly, the presentation learning module is used to extract classification features. Finally, the anti-interference classification module uses the output of the first two modules as labels and inputs for multi-task adversarial training to complete the representation learning that is not affected by the confounding of sites, so as to realize the adaptive anti-interference ASD classification. The results show that the average accuracy of ten-fold cross validation is 75.56%, which is better than the existing studies. The innovation of our proposed method lies in the problem that the traditional single-task deep learning ASD classification model will be affected by the heterogeneity of multi-site data and interfere with the classification. Our method eliminates the influence of multi-site factors on feature extraction through multi-task adversarial training, so that the model can better adapt to the heterogeneity of multi-site data. Meanwhile, large-scale 1DconV is introduced to extract features of brain functional network, which provides support for the interpretability of the model. This method is expected to take a

关键词： anti-interference neural networks multi-site data autoencoder large-scale 1DconV ASD

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Structural connectome alterations between individuals with autism and neurotypical controls using feature representation learning

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BEHAVIORAL AND BRAIN FUNCTIONS 2024年第1期20卷 1页

作者： Jang, Yurim Choi, Hyoungshin Yoo, Seulki Park, Hyunjin Park, Bo-yong Inha Univ Artificial Intelligence Convergence Res Ctr Incheon South Korea Sungkyunkwan Univ Dept Elect & Comp Engn Suwon South Korea Inst Basic Sci Ctr Neurosci Imaging Res Suwon South Korea Sungkyunkwan Univ Convergence Res Inst Suwon South Korea Sungkyunkwan Univ Sch Elect & Elect Engn Suwon South Korea Inha Univ Dept Data Sci Incheon South Korea

Autism spectrum disorder is one of the most common neurodevelopmental conditions associated with sensory and social communication impairments. Previous neuroimaging studies reported that atypical nodal- or network-level functional brain organization in individuals with autism was associated with autistic behaviors. Although dimensionality reduction techniques have the potential to uncover new biomarkers, the analysis of whole-brain structural connectome abnormalities in a low-dimensional latent space is underinvestigated. In this study, we utilized autoencoder-based feature representation learning for diffusion magnetic resonance imaging-based structural connectivity in 80 individuals with autism and 61 neurotypical controls that passed strict quality controls. We generated low-dimensional latent features using the autoencoder model for each group and adopted an integrated gradient approach to assess the contribution of the input data for predicting latent features during the encoding process. Subsequently, we compared the integrated gradient values between individuals with autism and neurotypical controls and observed differences within the transmodal regions and between the sensory and limbic systems. Finally, we identified significant associations between integrated gradient values and communication abilities in individuals with autism. Our findings provide insights into the whole-brain structural connectome in autism and may help identify potential biomarkers for autistic connectopathy.

关键词： Autism spectrum disorder autoencoder Feature representation learning Structural connectivity Integrated gradient

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