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An Architecture of Enhanced EMG signal Classification based on Multi-Agent System 3

An Architecture of Enhanced EMG Signal Classification based ...

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3rd international conference on Intelligent Engineering and Management, ICIEM 2022

作者： Jain, Reema Garg, Vijay Kumar Lovely Professional University Department of Computer Application Punjab Phagwara India Lovely Professional University Department of Computer Science and Engineering Punjab Phagwara India

ISBN: (数字)9781665467568

ISBN: (纸本)9781665467568

An Electromyography (EMG) signal provides reliable information and represents an important step towards man-machine communication to address neuro-muscular movements. The vital aspects of this research focus on data acquisition, signal pre-processing, feature extraction, and accurate classification to meet the practical challenges in terms of reliability. The recorded signals first undergo pre-processing in which the uploaded EMG signals are first segmented into clusters using K-means followed by a Genetic Algorithm (GA) that precisely extracts the rows that exhibit potential signal data. The multi-agent system is designed using a Support Vector Machine (SVM) with neural network architecture. This information of trained features deduced using SVM kernel function, also known as Support Vectors (SVs), is fed to Neural Network (NN) for the classification of EMG signals into two classes, namely, normal and pain. The performance of the proposed classification work is evaluated against the Electro- Myography-EMG dataset in terms of precision, sensitivity, f- measure, and accuracy. The simulation analysis over 1000 simulation rounds had demonstrated an average precision of 0.931, sensitivity of 0.915, f-measure of 0.923, and classification accuracy of 95.51%. The results illustrate a better classification accuracy when SVM support vectors are used by FFBPNN to learn the features of normal, and pain class in comparison to the existing EMG signal classification approaches. © 2022 IEEE.

关键词： Support vector machines

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Dance pose capture and recognition based on heterogeneous sensors 3

Dance pose capture and recognition based on heterogeneous se...

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3rd international conference on Machine Learning and Big data Analytics for IoT Security and Privacy, SPIoT2023

作者： Miao, Yan Baotou Teachers' College Baotou014030 China

In order to capture the movement posture of dancer in real time and effectively, a dancer motion and posture capture system based on heterogeneous sensors is proposed. Starting from the data processing of heterogeneous sensors in a single node, the single attitude measurement node of the system can be used to collect the initial posture data in the process of dancer motion in real time, achieve accurate capture of dancer movements, and filter and fuse the original motion data. In order to verify the performance and accuracy of the designed dancer movements capture system, relevant experiments are designed to verify it. The experimental results show that the algorithm designed in this experiment basically solves the interference problem of magnetic field and acceleration on attitude solution, and the attitude solution accuracy can basically meet the requirements. The fitting degree of the comparison between the extracted angle of the video sequence and the output angle of the sensor can basically meet the requirements, and the angle deviation is kept within 1 degree. The system can capture the dancer movements of the whole body and the hand, and the experimental results can meet the requirements basically, which has certain reference value. © 2023 The Authors. Published by Elsevier B.V.

关键词： data fusion

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A Heuristic Method for data Allocation and Task Scheduling on Heterogeneous Multiprocessor Systems Under Memory Constraints 23rd

A Heuristic Method for Data Allocation and Task Scheduling o...

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23rd international conference on Algorithms and Architectures for Parallel processing (ICA3PP)

作者： Ding, Junwen Song, Liangcai Li, Siyuan Wu, Chen He, Ronghua Su, Zhouxing Lu, Zhipeng Huazhong Univ Sci & Technol Wuhan Peoples R China Huawei Technol Co Ltd Lab 2012 Shenzhen Peoples R China

ISBN: (纸本)9789819708000;9789819708017

Computing workflows in heterogeneous multiprocessor systems are often depicted as directed acyclic graphs (DAGs) comprising tasks and data blocks. These elements represent computational modules and their interdependencies, where the output data generated by one task serves as input for other tasks. However, in certain workflows, such as task schedules in digital signal processors, it is essential to account for the memory capacity limitations of data blocks when scheduling the tasks. The main objective of this paper is to address the challenge of data allocation and task scheduling under memory constraints, particularly on shared memory platforms. We propose an integer linear programming model to formulate the problem, which can be viewed as an extension of the flexible job shop scheduling problem. The goal is to minimize the length of the critical path. We propose a tabu search algorithm (TS) to tackle the data allocation and task scheduling problem under memory constraints. The TS algorithm incorporates several distinguishing features, such as a greedy initial solution construction method and a mixed neighborhood evaluation strategy that combines exact and approximate evaluation methods. Experimental results on randomly generated instances demonstrate that the TS algorithm can obtain high-quality solutions within a reasonable computational time. On average, the TS algorithm improves the makespan by 5-25% compared to the widely used classical load balancing algorithms in the literature. Furthermore, we analyze some key features of the TS algorithm to identify the contributing factors to its success.

关键词： Task scheduling data allocation Heterogeneous multiprocessor Tabu search

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Framework for Real-Time Simulations of Routing Security Attacks in VANETs 2nd

Framework for Real-Time Simulations of Routing Security Atta...

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3rd international conference on Digital Technologies and Applications

作者： Ajjaj, Souad El Houssaini, Souad Hain, Mustapha El Houssaini, Mohammed-Alamine Hassan II Univ ENSAM Casablanca Morocco Chouaib Doukkali Univ Fac Sci Dept Comp Sci El Jadida Morocco Chouaib Doukkali Univ ESEF El Jadida Morocco

ISBN: (纸本)9783031298561;9783031298578

Intelligent transportation systems (ITS) rely on advanced networks, mainly vehicular ad hoc networks (VANETs). However, the deployment of VANETs in real world conditions is subject to a multitude of attacks that can severely impair the performance of the vehicular network and result in critical damage to people and materials. The primary purpose of this paper is to offer an experimental framework for real-time simulations of black hole attack inVANETs. The framework comprises three main components. First, the mobility component includes both OpenStreetMap and SUMO for generating realistic mobility models extracted from real world maps. The second component makes use of the network simulator NS-3 to model the entire protocol stack of VANET communications. A third component is devoted to data collection based on the monitoring of the vehicular network in real time by assessing the most important performance metrics, mainly throughput, lost packets and routing overhead. In this component, we suggest using a simple and reliable data extraction mechanism instead of processing the NS-3 ASCII trace files, often requiring higher processing time and computational capabilities. Our proposed framework can potentially be deployed for conducting real-time simulations of routing attacks in VANETs and evaluating their impact on the network throughput. To evaluate the feasibility of the proposed framework, extensive simulations of the AODV routing protocol are carried out where a black hole attack is launched.

关键词： VANETs Simulation NS-3 SUMO AODV Back hole

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3rd EAI international conference on Edge Computing and IoT, EAI ICECI 2022

3rd EAI International Conference on Edge Computing and IoT, ...

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3rd EAI international conference on Edge Computing and IoT, EAI ICECI 2022

ISBN: (纸本)9783031289897

The proceedings contain 21 papers. The special focus in this conference is on Edge Computing and IoT. The topics include: Evaluation of Higher Education System;defense Mechanisms Against Audio Adversarial Attacks: Recent Advances and Future Directions;an Empirical Study of Worldwide Plastic Waste Mitigation;prediction for Surface Subsidence of Shield Construction in Water-Rich Sand Egg Stratum Based on Edge Intelligence;highly Accurate Dynamic Gesture Recognition Method Based on Edge Intelligence;adversarial Example Attacks in Internet of Things (IoT);training Node Screening in Decentralized Trusted Federated Learning;exploration and Practice of Course Homework Metaverse Based on Extended Reality Under Edge Computing;federated Learning Based User Scheduling for Real-Time Multimedia Tasks in Edge Devices;the data Exchange Protocol over Multi-chain Blockchain Using Zero-Knowledge Proof;a Co-caching Strategy for Edges Based on Federated Learning and Regional Prevalence;LSTM-DAM: Malicious Network Traffic Prediction for Cloud Manufacturing System;a Hybrid Task Offloading and Service Cache Scheme for Vehicular Edge Computing;on Enhancing Transmission Performance for IoV Based on Improved Greedy Algorithm;Precise Segmentation on Poly-Yolo, YoloV5 and FCN;Research on DAG Based Consensus Mechanism for Adjustable Load Metering data;a Detection and Information processing Model Based on an Automatic Mechanism for Tax Payment Control in Developing Countries;IEC-FOF: An Industrial Electricity Consumption Forecasting and Optimization Framework;demons Hidden in the Light: Unrestricted Adversarial Illumination Attacks.

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A Study of data Governance Methods Based on Non-Linear Evaluation of K-Domains 3

A Study of Data Governance Methods Based on Non-Linear Evalu...

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3rd international conference on Artificial Intelligence, Big data and Algorithms, CAIBDA 2023

作者： Li, Yajie Sun, Kaining Ming, Tao Chen, Anqing State Grid Xinjiang Electric Power Co. Ltd. Information and Communication Company Xinjiang Urumqi832000 China Xinjiang Energy Internet Big Data Laboratory Xinjiang Urumqi832000 China State Grid Xinjiang Electric Power Co. Ltd Xinjiang Urumqi832000 China State Grid Xintong Yili Technology Co. Ltd. Fujian Fuzhou350000 China

ISBN: (纸本)9781643684444

The current traditional data governance methods mainly ensure the overall quality of data through data quality inspection, and the lack of data fusion processing leads to poor data governance results. In this regard, a data governance method based on nonlinear evaluation of K domain is proposed. By formulating data cleaning rules, data are processed such as weight reduction, gap filling and deletion, and data fusion operations are performed according to the fusion table attributes by configuring fusion rules. Finally, the data consistency as well as wholeness is checked, and the actual effect of data governance is determined by the data quality score results. In the experiments, the proposed method is validated for the governance effect. The analysis of the experimental results shows that the proposed method takes significantly shorter time for data fusion and has better data governance performance when the proposed method is used to govern multiple sources of data. © 2023 The Authors.

关键词： data fusion

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Automated Detection of Type 2 Diabetes with Imbalanced and Machine Learning Methods 3rd

Automated Detection of Type 2 Diabetes with Imbalanced and M...

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3rd international conference on Machine Learning, Image processing, Network Security and data Sciences, MIND 2021

作者： Anirudh, G. Talukdar, Upasana Department of Data Science and Analytics Central University of Rajasthan Rajasthan Ajmer India Department of Computer Science and Engineering Indian Institute of Information Technology Guwahati Guwahati India

ISBN: (纸本)9789811958670

Diabetes mellitus is an abominable disease, if left untreated, may turn fatal. This paper presents rigorous experimentation to ascertain a machine learning model that can implement an accurate diagnosis of diabetes mellitus. While many research efforts have been publicized till date to detect diabetes, none of them addresses the class imbalance problem. This paper carries out the experiments on Pima Indian Diabetes in three categories a. experiments with traditional machine learning algorithms, b. experiments with ensemble machine learning methods, and c. experiments with machine learning algorithms after imbalanced data pre-processing techniques. The performance of the solutions has been evaluated using F1-score, Precision, Recall, Area Under Precision-Recall curve, Area Under Receiver Operating Characteristic curve, and Classification Accuracy. The experimental results show improved performance with the proposed approach as compared to the state-of-the-art methods. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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New Strategy for Unbalance Compensation Based on PWM AC-Chopper for Railway Applications 1

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3rd international conference on Electronic Engineering and Renewable Energy, ICEERE 2022

作者： Ismail, Mir Anas, Benslimane Jamal, Bouchnaif Badreddine, Lahfaoui Yassine, Ayat Mimoun, Yandouzi Renewable Energy Embedded System and Data Processing Laboratory National School of Applied Sciences Mohamed First University Oujda Morocco Laboratory of Electrical Engineering and Maintenance Higher School of Technology University Mohammed I Oujda Oujda Morocco

ISBN: (数字)9789811962233

ISBN: (纸本)9789811962226

This paper focuses on the problem of voltage unbalance for single-phase railway substations using the AC-chopper controlled impedance concept (CCI). This topology is based on the use of an active Steinmetz circuit. The sizing and control method of the compensator has been studied based on real data provided by the Moroccan railway office (ONCF). A comparison was realized between the CCI compensator, voltage source inverter-based STATCOM VSI, and current source inverter-based STATCOM CSI in terms of power losses in the semiconductors. The three topologies have been implemented in the MATLAB/Simulink environment. The obtained results prove the efficiency of the CCI topology compared to the VSI and CSI compensators. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Topology

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Performance Optimization and Real-Time Application of English Machine Translation Driven by Big data 3

Performance Optimization and Real-Time Application of Englis...

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3rd international conference on Electronic InformationTechnology and Smart Agriculture, ICEITSA 2023

作者： Chen, Lei School of Foreign Languages Sichuan University Jinjiang College China

ISBN: (纸本)9798400716775

The growth of computers has allowed machine translation, which has its roots in NLP, to be applied in a wide variety of settings. The increasing need for translation between languages has highlighted the need of addressing the accuracy of text translation. The development of big data technologies has resulted in a wealth of data suitable for training machine translators. With the help of recent developments in big data and natural language processing, neural network technology is finding widespread use in the field of machine translation. This allows neural machine translation methods to eventually replace their more traditional counterparts. In this paper, we use neural network technology to investigate the state of English-Chinese multilingual translation. To begin, this paper suggests a technique for split-second multitasking. It investigates the best ways to combine different attention calculation methods in order to reap the benefits of all of them. Second, this study suggests a refined LSTM. The network can effectively combine prospective and retrospective data to get more reliable contextual semantic understanding. Third, this study presents a multi-subspace attention mechanism, which utilizes numerous attention computation functions to calculate an attention score and maps the hidden layer states of the ILSTM to many subspaces. Finally, this study constructs a translation model by combining a multi-attention mechanism, an enhanced LSTM, and a multi-subspace attention mechanism. Finally, this study systematically tests the proposed bilingual translation approach, and results from these trials verify the method's viability and its real-time performance. © 2023 ACM.

关键词： Big data

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A Comparison of Different signal processing Techniques for Upper Limb Muscle Activity Onset Detection using Surface Electromyography

A Comparison of Different Signal Processing Techniques for U...

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international Symposium on Artificial Intelligence and signal processing (AISP)

作者： Pratap Kumar Koppolu Krishnan Chemmangat Electrical & Electronics Engineering Department NIT Karnataka Surathkal Mangalore India

This work presents the use of real-time experimental Surface Electromyography (sEMG) signals to determine muscle activity of upper limb by detecting the exact onset and offset timings. Various muscle activity detection methods were evaluated, such as Sample Entropy (SEn), Permutation Entropy (PEn), Amplitude Aware Permutation Entropy (AAPEn), and Integrated Profile (IP). The performance of these methods was compared, and it was found that IP detects muscle activity quickly and requires less computation for real-time implementation as compared to other methods.

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