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A Novel Locomotion Rule Rmbedding Long Short-Term Memory Network with Attention for Human Locomotor Intent Classification Using Multi-Sensors Signals

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computers, Materials & Continua 2024年第6期79卷 4349-4370页

作者： Jiajie Shen Yan Wang Dongxu Zhang Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of EducationColleague of Computer Science and TechnologyJilin UniversityChangchun130012China College of Software and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of EducationJilin UniversityChangchun130012China

Locomotor intent classification has become a research hotspot due to its importance to the development of assistive robotics and wearable *** work have achieved impressive performance in classifying steady locomotion ***,it remains challenging for these methods to attain high accuracy when facing transitions between steady locomotion *** to the similarities between the information of the transitions and their adjacent steady ***,most of these methods rely solely on data and overlook the objective laws between physical activities,resulting in lower accuracy,particularly when encountering complex locomotion modes such as *** address the existing deficiencies,we propose the locomotion rule embedding long short-term memory(LSTM)network with Attention(LREAL)for human locomotor intent classification,with a particular focus on transitions,using data from fewer sensors(two inertial measurement units and four goniometers).The LREAL network consists of two levels:One responsible for distinguishing between steady states and transitions,and the other for the accurate identification of locomotor *** classifier in these levels is composed of multiple-LSTM layers and an attention *** introduce real-world motion rules and apply constraints to the network,a prior knowledge was added to the network via a rule-modulating *** method was tested on the ENABL3S dataset,which contains continuous locomotion date for seven steady and twelve transitions *** results showed that the LREAL network could recognize locomotor intents with an average accuracy of 99.03%and 96.52%for the steady and transitions states,*** is worth noting that the LREAL network accuracy for transition-state recognition improved by 0.18%compared to other state-of-the-art network,while using data from fewer sensors.

关键词： Lower-limb prosthetics deep neural networks motion classification

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Intelligent COVID-19 forecasting, diagnoses and monitoring systems: A survey

TechRxiv

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TechRxiv 2021年

作者： Anjum, Nasreen Asif, Amna Kiran, Mehreen Jabeen, Fouzia Yang, Zhahoui Huang, Chongwen Noor, Salma Imran, Kanwal Ali, Ihsan Mohamed, Ehab Mahmoud The Department of Computer Science Shaheed Benazir Bhutto Women University Peshawar Pakistan The Department of Electronic and Electrical Engineering University College London LondonWC1E 6BT United Kingdom College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China International Joint Innovation Center Zhejiang University Haining314400 China Hangzhou310027 China Faculty of Computer Science and Information Technology University of Malaya Malaysia Electrical Engineering Dept. Colleague of Engineering Prince Sattam Bin Abdulaziz University Wadi Addwasir Saudi Arabia Electrical Engineering Dept. Faculty of Engineering Aswan University Aswan Egypt

To date, the novel Corona virus (SARS-CoV-2) has infected millions and has caused the deaths of thousands of people around the world. At the moment, five antibodies, two from China, two from the U.S., and one from the UK, have already been widely utilized and numerous vaccines are under the trail process. In order to reach herd immunity, around 70% of the population would need to be inoculated. It may take several years to hinder the spread of SARS-CoV-2. Governments and concerned authorities have taken stringent measurements such as enforcing partial, complete, or smart lockdowns, building temporary medical facilities, advocating social distancing, and mandating masks in public as well as setting up awareness campaigns. Furthermore, there have been massive efforts in various research areas and a wide variety of tools, technologies and techniques have been explored and developed to combat the war against this pandemic. Interestingly, machine learning algorithms and internet of Things (IoTs) technology are the pioneers in this race. Up till now, several real-time and intelligent COVID-19 forecasting, diagnosing, and monitoring systems have been proposed to tackle the COVID-19 pandemic. In this article based on our extensive literature review, we provide a taxonomy based on the intelligent COVID-19 forecasting, diagnosing, and monitoring systems. We review the available literature extensively under the proposed taxonomy and have analyzed a significantly wide range of machine learning algorithms and IoTs which can be used in predicting the spread of COVID-19 and in diagnosing and monitoring the infected individuals. Furthermore, we identify the challenges and also provide our vision about the future research on COVID-19. © 2021, CC BY.

关键词： Coronavirus

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Combined group method of data handling models using artificial bee colony algorithm in time series forecasting 16

Combined group method of data handling models using artifici...

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16th International Learning and Technology Conference, L and T 2019

作者： Yahya, Nurhaziyatul A. Samsudin, Ruhaidah Shabri, Ani Saeed, Faisal School of Computing Faculty of Engineering Universiti Teknologi Malaysia Johor Bahru Johor81310 Malaysia School of Mathematic Faculty of Science Universiti Teknologi Malaysia Johor Bahru Johor81310 Malaysia Colleague of Computer Science and Engineering Taibah University Medina Saudi Arabia

Time series forecasting is an important area of forecasting which has gained many attentions from various research areas. In line with its popularity, various models have been introduced for the purpose of producing accurate time series forecasts. Nevertheless, it is difficult to find an ideal model as there is no model that can perform best for all types of data. Recently, combination of forecasts has gained immense popularity in the time series forecasting area. Among the well-known combination technique is the weighted-based approach, where appropriate weights are given based on the performance of each individual model. In this paper, a robust methodology for accurate time series forecasting based on Artificial Bee Colony (ABC) algorithm and four Group Method of Data Handling (GMDH) models, namely GMDH-Polynomial, GMDH-Radial Basis Function, GMDH-Sigmoid and GMDH-Tangent are proposed. In this methodology, the first step was to develop forecasts using individual GMDH models. In the second step, weights for each individual GMDH were calculated heuristically using ABC algorithm. In the final step, the results were aggregated to form a new forecast. For the purpose of evaluating the performance of the proposed model, the model was applied to a real time series data which is the monthly tourist arrivals from Singapore to Malaysia from year 2000 to 2017. Based on the empirical results, the application of nonlinear transfer function such as Tangent function has the potential to improve the performance of GMDH model, while the other functions such as Sigmoid function produced a forecast which is worse than the conventional GMDH-Polynomial. However, the combination of GMDH models using ABC managed to outperform all the individual models. The empirical finding demonstrated the efficiency of Combined GMDH model in time series forecasting, as well as the applicability of implementing optimization algorithm to find appropriate weights for the individual models. © 2019 The Aut

关键词： Time series

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Combined group method of data handling models using artificial bee colony algorithm in time series forecasting

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Procedia computer science 2019年 163卷 319-329页

作者： Nurhaziyatul A. Yahya Ruhaidah Samsudin Ani Shabri Faisal Saeed School of Mathematic Faculty of Science Universiti Teknologi Malaysia 81310 Johor Bahru Johor Malaysia Colleague of Computer Science and Engineering Taibah University Medina Saudi Arabia

关键词： Artificial bee colony combined models forecasting group method of data handling time series weighted-based

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Detail-enhanced medical image fusion in NSCT domain

International Journal of Multimedia and Ubiquitous Engineeri...

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International Journal of Multimedia and Ubiquitous engineering 2015年第8期10卷 171-180页

作者： Yang, Guocheng Chen, Leiting Li, Meiling School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Department of Biomedical Engineering Luzhou Medical Colleague Zhongshan Road LuzhouSichuan China Provincial Key Laboratory of Digital Media Chengdu China School of Life Science and Technology University of Electronic Science and Technology of China Chengdu China

Multimodal medical image fusion technique plays an important role in clinical applications, such as pathologic diagnosis and surgical options. However, many traditional fusion methods cannot well preserve details of source images in the fused image. To address this problem, a detail-enhanced image fusion scheme based on non-subsampled contourlet transform (NSCT) and gain control (i.e., NCGC) is developed in this paper, which can effectively combine the spectral information and the spatial features of source images. The proposed method applies power law transformation to tune coefficients of each decomposed subband, and adjusts the strength of subband signals by smooth gain control. Eventually, the fused image with more anatomical details and functional information is constructed by the inverse NSCT. Three pairs of medical images with different modalities and three fusion metrics are applied to validate the feasibility of this algorithm. Experimental results demonstrate that the proposed method can achieve superior performance in both visual perception and objective assessment. © 2015 SERSC.

关键词： Contourlet transform

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RETRACTED ARTICLE: An analysis for particle trajectories of a discrete particle swarm optimization

RETRACTED ARTICLE: An analysis for particle trajectories of ...

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作者： Tao, Qian Chang, Hui-You Yi, Yang Gu, Chun-Qin Li, Wen-Jie Department of Computer Science Sun Yat-sen University Guangzhou 510006 China Guangzhou University Sontan Colleague China Department of Computer Science Zhongkai University of Agriculture and Engineering Guangzhou 510225 China

ISBN: (纸本)9781424455386

As the theoretical studies of discrete particle swarm optimization (PSO) are extremely complex and diversity, empirical analysis and validation are the primary research method as always for all type of discrete PSO algorithms. This paper extends these studies to a theoretical analysis for a discrete PSO particle trajectory, and also provides a formal proof that each particle converges to a stable point. Experimental results are provided to support the conclusions drawn from the theoretical findings. © 2010 IEEE.

关键词： Particle swarm optimization (PSO)

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A trustworthy management approach for cloud services QOS data

A trustworthy management approach for cloud services QOS dat...

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International Conference on Machine Learning and Cybernetics

作者： Tao, Qian Chang, Huiyou Yi, Yang Gu, Chunqin Department of Computer Science Sun Yat-sen University Guangzhou 510275 China Sontan Colleague Guangzhou University Guangzhou 511370 China Department of Computer Science Zhongkai University of Agriculture and Engineering Guangzhou 510225 China

ISBN: (纸本)9781424465262

In cloud computing environments, computing power, storage, platforms, and software are abstracted and virtualized as all kind of cloud services. Meanwhile complex applications can be described as processes invoking services selected at runtime. As a nonfunctional requirement, quality-of-service (QoS) is an important selection basis of cloud services. Nevertheless, the complex network environment and illegal operations generally result in the untrustworthy QoS data in reality, and then influence the preciseness and trustworthiness of cloud services scheduling. This paper proposes a trustworthy management approach for cloud services QoS. Results of experiments demonstrate the effectiveness of the proposed trustworthy management approach. © 2010 IEEE.

关键词： Quality of service

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A grid workflow scheduling optimization approach for e-business application

A grid workflow scheduling optimization approach for e-busin...

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1st International Conference on E-Business and E-Government, ICEE 2010

ISBN: (纸本)9780769539973

Grid workflow scheduling in large scale e-Business applications is a complex optimization problem which may require consideration of different scheduling criteria to achieve various quality of service (QoS) requirements. Meanwhile, the researches on grid workflow scheduling mainly focus on the constraint of time and cost, the key requirements about trustworthiness aren't considered adequately. Aiming at this problem, a new trustworthiness-based scheduling model is proposed for the grid workflows scheduling which is muilti-objective optimization problem in general, meanwhile, A integer genetic algorithm(IGA) is presented to optimize the scheduling *** simulation results show that as a robust, multi-criteria scheduling algorithms, the performance of IGA is very promising. The scheduling can succeed in open and dynamic grid environments, and its result in e-Business Application is trustworthiness and robustness. © 2010 IEEE.

关键词： Scheduling

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A trustworthy management approach for cloud services QoS data

A trustworthy management approach for cloud services QoS dat...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Qian Tao Huiyou Chang Yang Yi Chunqin Gu Department of Computer Science Sun Yat-sen University Guangzhou China Sontan Colleague Guangzhou University Guangzhou China Department of Computer Science Zhongkai University of Agriculture and Engineering Guangzhou China

关键词： Quality of service Clouds Distributed databases Cloud computing Clustering algorithms Computational modeling Processor scheduling

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Notice of Retraction: An analysis for particle trajectories of a discrete particle swarm optimization

Notice of Retraction: An analysis for particle trajectories ...

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International Conference on computer science and Information Technology (CSIT)

作者： Qian Tao Hui-you Chang Yang Yi Chun-qin Gu Wen-jie Li Department of Computer Science Sun Yat-sen University Guangzhou China Sontan Colleague Guangzhou University China Department of Computer Science Zhongkai University of Agriculture and Engineering Guangzhou China

This article has been retracted by the publisher.

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