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Musculoskeletal Injury Recovery Assessment using Gait Analysis with Ground Reaction Force Sensor

MEDICAL ENGINEERING & PHYSICS 2022年第0期103卷 103788-103788页

作者： Chakraborty, Jayeeta Upadhyay, Shashankesh Nandy, Anup NIT Rourkela Dept Comp Sci & Engn Machine Intelligence & Biomot Res Lab Rourkela Orissa India

Patients with musculoskeletal injuries are required to exhibit specific tests for clinicians to monitor the recovery progress during the rehabilitation period. An automated system tracking the progress of an injured patient is essential for emerging applications in the healthcare domain. In this study, we propose a long short-term memory cell-based auto-encoder model (LSTM-AE) for the recovery assessment using data collected during walking. A recently published ground reaction force (GRF) dataset of musculoskeletal impairments, GaitRec, is used for this study. The model is trained to learn the gait pattern of healthy individuals. The model is then tested with GRF signals collected during different sessions of rehabilitation from injured patients. The reconstruction losses generated using the GRF signals are assessed to indicate the recovery status of the patient. The results analysis suggests that the reconstruction loss of the LSTM-AE model gradually reduces towards the ending phase of recovery. Similar trend is observed for both legs in all categories of impairments indicating gradual improvement in patients.

关键词： Musculoskeletal injuries recovery prediction gait analysis auto-encoder long short-term memory network ground reaction force

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Federated Intrusion Detection In NG-IoT Healthcare Systems: An Adversarial Approach

Federated Intrusion Detection In NG-IoT Healthcare Systems: ...

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IEEE International Conference on Communications (ICC)

作者： Siniosoglou, Ilias Sarigiannidis, Panagiotis Argyriou, Vasilis Lagkas, Thomas Goudos, Sotirios K. Poveda, Maria Univ Western Macedonia Dept Elect & Comp Engn Kozani Greece Kingston Univ Dept Networks & Digital Media Kingston Upon Thames Surrey England Int Hellen Univ Dept Comp Sci Thessaloniki Greece Aritostle Univ Thessaloniki Phys Dept Thessaloniki Greece Sidroco Holdings Ltd CY-1077 Nicosia Cyprus

ISBN: (纸本)9781728171227

In recent years and with the advancement of IoT networks, malicious intrusions aiming at disrupting the services and getting access to confidential information in medical environments is ever progressing. To that end, this paper proposes a Federated Layered Architecture to be used in Medical Cyber-Physical Systems (MCPS) Networks that entails the creation of multiple aggregation layers to induce further security to the model training process. Moreover, two Deep Adversarial Neural Networks (GANs) are presented for use with data found in the MCPS environment. The evaluation of the presented work showed that the models trained in the Federated system have an increase in their ability to detect possible intrusions in the MCPS network than the commonly trained models.

关键词： Anomaly Detection auto-encoder Cybersecurity Generative Adversarial Network Deep Learning Machine Learning Critical Infrastructures Medical Cyber-Physical Systems

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False Data Injection Attack Detection in Smart Grid Using Energy Consumption Forecasting

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ENERGIES 2022年第13期15卷 4877-4877页

作者： Mahi-al-rashid, Abrar Hossain, Fahmid Anwar, Adnan Azam, Sami Islamic Univ Technol Dept Mech & Prod Engn KB Bazar Rd Gazipur 1704 Bangladesh Deakin Univ Sch IT 75 Pigdons Rd Geelong Vic 3216 Australia Charles Darwin Univ Coll Engn IT & Environm Casuarina 0810 Australia

Supervisory Control and Data Acquisition (SCADA) systems are essential for reliable communication and control of smart grids. However, in the cyber-physical realm, it becomes highly vulnerable to cyber-attacks like False Data Injection (FDI) into the measurement signal which can circumvent the conventional detection methods and interfere with the normal operation of grids, which in turn could potentially lead to huge financial losses and can have a large impact on public safety. It is imperative to have an accurate state estimation of power consumption for further operational *** work presents novel forecasting-aided anomaly detection using an CNN-LSTM based auto-encoder sequence to sequence architecture to combat against false data injection attacks. We further present an adaptive optimal threshold based on the consumption patterns to identify abnormal behaviour. Evaluation is performed on real-time energy demand consumption data collected from the Australian Energy Market Operator. An extensive experiment shows that the proposed model outperforms other benchmark algorithms in not only improving the data injection attack (95.43%) but also significantly reducing the false positive rate.

关键词： smart grid deep learning auto-encoder false data injection cyber security anomaly detection

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Power System Event Detection using auto-encoders and the Fourier Transform

Power System Event Detection using Auto-encoders and the Fou...

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Modern Electric Power Systems (MEPS) International Conference

作者： Dawidowski, Pawel Sipowicz, Jakub Balcerek, Przemyslaw Burek, Arkadiusz Smolana, Michal ABB Corp Res Krakow Poland

ISBN: (纸本)9781728116419

Access to raw substation data in a digital form allows atypical events, which could potentially be indicative of future performance degradation or fault occurrence, to be identified. Methods where the storage of relevant data is triggered after an event has been detected can significantly reduce the amount of data to be stored. In order to propose such a system it is necessary to automatically detect power system events. In this paper, several algorithms for event or anomaly detection in electrical power systems are investigated and compared. The proposed algorithms utilize unsupervised training of auto-encoders and Fourier transform-based signal reconstruction. The algorithms are tested on a hand crafted benchmark signal, simulation-generated transients during typical faults and on available recorded events. The performance of the algorithms is compared with respect to event detection sensitivity and computational power required to run each algorithm in real time.

关键词： power system faults fault recorder event detection anomaly detection auto-encoder

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A Convolution-Based System for Malicious URLs Detection

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Computers, Materials & Continua 2020年第1期62卷 399-411页

作者： Chaochao Luo Shen Su Yanbin Sun Qingji Tan Meng Han Zhihong Tian Institute of Computer Application China Academy of Engineering PhysicsMianyang621054China Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhou510006China School of Mechanical and Electrical Engineering Heilongjiang State Farm Science Technology Vocational CollegeHarbin150431China Department of Computing and Software Engineering Kennesaw State UniversityKennesawGA 30144USA

Since the web service is essential in daily lives,cyber security becomes more and more important in this digital *** Uniform Resource Locator(URL)is a common and serious threat to *** hosts unsolicited content and lure unsuspecting users to become victim of scams,such as theft of private information,monetary loss,and malware ***,it is imperative to detect such ***,traditional approaches for malicious URLs detection that based on the blacklists are easy to be bypassed and lack the ability to detect newly generated malicious *** this paper,we propose a novel malicious URL detection method based on deep learning model to protect against web ***,we firstly use auto-encoder to represent ***,the represented URLs will be input into a proposed composite neural network for *** order to evaluate the proposed system,we made extensive experiments on HTTP CSIC2010 dataset and a dataset we collected,and the experimental results show the effectiveness of the proposed approach.

关键词： CNN anomaly detection web security auto-encoder deep learning

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Analysis on block chain financial transaction under artificial neural network of deep learning

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JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS 2020年 380卷 112991-112991页

作者： Gao, Wenyou Su, Chang Changchun Univ Sci & Technol Sch Econ & Management Changchun 130022 Peoples R China Harbin Inst Technol Sch Foreign Language Harbin 150000 Peoples R China

In order to conduct an in-depth study on financial transactions of block chain, the classical back propagation (BP) neural network based on the artificial neural network (ANN) model is selected, and its propagation mode, weight change, and learning process are analyzed. For the problem of slow convergence speed and local minimum value of BP neural network, based on the idea of deep learning, the initial value and training step are changed by auto-encoder and restricted Boltzmann machine, and the theory is analyzed. Taking the stock index futures trading in the block chain financial trading as an example, the stock price trading of stock index futures is studied using the two deep learning neural network models to predict the price changes. The results show that the auto-encoder, as an unsupervised learning system, performs better than the restricted Boltzmann machine in setting the initial weights and thresholds, with fewer iterations, faster convergence rate, and smaller convergence error. The results obtained by the auto-encoder can be used as initialization settings and data analysis. The prediction accuracy of the whole model is around 59%. When the transaction cost is not considered, the transaction can be conducted based on the prediction signal of the deep learning model. Therefore, deep learning neural network model can be applied to block chain financial transactions as a reference for financial transactions, which has a good practical significance for the development of this field. (C) 2020 Elsevier B.V. All rights reserved.

关键词： BP neural network auto-encoder Restricted Boltzmann machine Deep learning

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Simultaneous Spatial-Temporal Decomposition of Connectome-Scale Brain Networks by Deep Sparse Recurrent auto-encoders 26th

Simultaneous Spatial-Temporal Decomposition of Connectome-Sc...

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26th Biennial International Conference on Information Processing in Medical Imaging (IPMI)

作者： Li, Qing Dong, Qinglin Ge, Fangfei Qiang, Ning Zhao, Yu Wang, Han Huang, Heng Wu, Xia Liu, Tianming Beijing Normal Univ Beijing Peoples R China Univ Georgia Athens GA 30602 USA Shaanxi Normal Univ Xian Shaanxi Peoples R China Zhejiang Univ Hangzhou Zhejiang Peoples R China Northeastern Polytech Univ Xian Shaanxi Peoples R China

ISBN: (纸本)9783030203511;9783030203504

Exploring the spatial patterns and temporal dynamics of human brain activities has long been a great topic, yet development of a unified spatial-temporal model for such purpose is still challenging. To better understand brain networks based on fMRI data and inspired by the success in applying deep learning for brain encoding/decoding, we propose a novel deep sparse recurrent auto-encoder (DSRAE) in an unsupervised spatial-temporal way to learn spatial and temporal patterns of brain networks jointly. The proposed DSRAE has been validated on the publicly available human connectome project (HCP) fMRI datasets with promising results. To our best knowledge, the proposed DSRAE is among the early unified models that can extract connectome-scale spatial-temporal networks from 4D fMRI data simultaneously.

关键词： Task-based fMRI Recurrent neural network auto-encoder

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Self-Learning and Efficient Health-Status Analysis for a Core Router System

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 2020年第9期39卷 1935-1948页

作者： Jin, Shi Zhang, Zhaobo Chakrabarty, Krishnendu Gu, Xinli Duke Univ Dept Elect & Comp Engn Durham NC 27708 USA Nvidia Corp Santa Clara CA 95051 USA Futurewei Res & Dev USA Futurewei Technol Santa Clara CA 95050 USA

The health status of core router systems needs to be analyzed efficiently in order to ensure high reliability and timely error recovery. Although a large amount operational data is collected from core routers, due to high computational complexity and expensive labor cost, only a small part of this data is labeled by experts. The lack of labels is an impediment toward the adoption of supervised learning. We present an iterative self-learning procedure for assessing the health status of a core router. This procedure first computes a representative feature matrix to capture different characteristics of time-series data. Not only statistical-modeling-based features are computed from three general categories but also a recurrent neural network-based autoencoder is utilized to capture a wider range of hidden patterns. Moreover, both minimum-redundancy-maximum-relevance (mRMR) method and fully connected feedforward autoencoder are applied to further reduce dimensionality of extracted feature matrix. Hierarchical clustering is then utilized to infer labels for the unlabeled dataset. Finally, a classifier is built and iteratively updated using both labeled and unlabeled dataset. Field data collected from a set of commercial core routers are used to experimentally validate the proposed health-status analyzer. The experimental results show that the proposed feature-based self-learning health analyzer achieves higher precision and recall than the traditional supervised health analyzer as well the currently deployed rule-based health analyzer. Moreover, it achieves better performance than the three anomaly detection baseline methods under the transformed binary classification scenario.

关键词： Feature extraction Time series analysis Monitoring Anomaly detection Detectors Measurement Fault tolerance auto-encoder core router systems feature extraction and selection machine-learning techniques self-learning health-status analysis time-series analysis

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Application of deep learning for retinal image analysis: A review

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COMPUTER SCIENCE REVIEW 2020年 35卷 100203-100203页

作者： Badar, Maryam Haris, Muhammad Fatima, Anam Natl Univ Sci & Technol Sch Elect Engn & Comp Sci Islamabad Pakistan Univ Engn & Technol Lahore Pakistan

Retinal image analysis holds an imperative position for the identification and classification of retinal diseases such as Diabetic Retinopathy (DR), Age Related Macular Degeneration (AMD), Macular Bunker, Retinoblastoma, Retinal Detachment, and Retinitis Pigmentosa. automated identification of retinal diseases is a big step towards early diagnosis and prevention of exacerbation of the disease. A number of state-of-the-art methods have been developed in the past that helped in the automatic segmentation and identification of retinal landmarks and pathologies. However, the current unprecedented advancements in deep learning and modern imaging modalities in ophthalmology have opened a whole new arena for researchers. This paper is a review of deep learning techniques applied to 2-D fundus and 3-D Optical Coherence Tomography (OCT) retinal images for automated classification of retinal landmarks, pathology, and disease classification. The methodologies are analyzed in terms of sensitivity, specificity, Area under ROC curve, accuracy, and F score on publicly available datasets which includes DRIVE, STARE, CHASE_DB1, DRiDB, NIH AREDS, ARIA, MESSIDOR-2, E-OPTHA, EyePACS-1 DIARETDB and OCT image datasets. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Deep learning Deep neural network Convolutional neural network auto-encoder Sparse stacked auto-encoder De-noised sparse auto-encoder Softmax Random forest Rectified linear unit Hidden layers

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Modular domain-to-domain translation network

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NEURAL COMPUTING & APPLICATIONS 2020年第11期32卷 6779-6791页

作者： Karatsiolis, Savvas Schizas, Christos N. Petkov, Nicolai Univ Cyprus Univ Ave 1 CY-2109 Nicosia Cyprus Univ Groningen Bernoulli Inst Math Comp Sci & Artificial Intelli Dept Intelligent Syst Grp NL-9712 CP Groningen Netherlands

Domain-to-domain translation methods map images from a source domain to corresponding images from a target domain. The two domains contain images from the same classes, but these images look different. Recent approaches use generative adversarial networks in various configurations and architectures to perform the translation. By using GANs, they inevitably inherit their problems like training instability and mode collapse. We propose a novel approach to the problem that does not use a GAN. Instead, it relies on an hierarchical architecture that encapsulates information of the target domain by using individually trained networks. This hierarchical architecture is then trained as one unified deep network. Using this approach, we show that images from the original domain are translated to the target domain both for the case when there is a one-to-one correspondence between the images of the two domains and for the case that such correspondence information is absent. We visualize and evaluate the translation from one information domain to the other and discuss the proposed model's relation to the conditional generative adversarial networks. We further argue that deep learning can benefit from the proposed hierarchical architecture.

关键词： Unsupervised learning auto-encoder Feature mapping Neural networks

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