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检索条件"任意字段=2021 10th International Conference on Computing and Pattern Recognition"

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Possibility of Managing Medical device Post-market Surveillance using Artificial Intelligence and Standardized Methodology 10

Possibility of Managing Medical device Post-market Surveilla...

引用

10th Mediterranean conference on Embedded computing, MECO 2021

作者： Pokvic, Lejla Gurbeta Deumic, Amar Lutovac, Budimir Badnjevic, Almir Medical Device Inspection Laboratory Verlab Ltd. International Burch University Faculty of Natural Sciences and Engineering Department for Genetics and Bioengineering Sarajevo Bosnia and Herzegovina University of Montenegro Faculty of Electrical Engineering Montenegro

ISBN: (纸本)9780738133614

the activities related to medical device market can be divided into pre-market and post-market surveillance. Pre-market processes have been defined by Medical Device Directives since 1992. these directives define all aspects of medical device design, production, testing, approval and certification. Appropriate standards have been adopted to support these activities. the CE mark issued by European Notified Bodies confirms that the medical device complies with safety and performance standards related to its class and that is therefore safe for intended usage. Post-market surveillance is not as well defined, so the new Medical Device Regulation addresses the identified gap and emphasizes the importance of standardizing and harmonizing the system for surveillance of medical devices already in use. the MDRs require stakeholders to monitor the quality, performance and safety of a device throughout its life cycle and to take corrective or preventive action when necessary. In this paper, we discuss the possibility of using artificial intelligence on Big Data structures resulting from the comprehensive methodology of post-market surveillance. © 2021 IEEE.

关键词： Artificial intelligence

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Ciphertext-Policy Attribute-Based Encryption for General Circuits in Cloud computing 10

Ciphertext-Policy Attribute-Based Encryption for General Cir...

引用

10th international conference on Control, Automation and Information Sciences, ICCAIS 2021

作者： Wu, Qing Li, Liangjun Xi'an University of Posts and Telecommunications School of Automation Xi'an China

ISBN: (纸本)9781665440295

Driven by the development of Internet and information technology, cloud computing has been widely recognized and accepted by the public. However, with the occurrence of more and more information leakage, cloud security has also become one of the core problem of cloud computing. As one of the resolve methods of it, ciphertext-policy attribute-based encryption (CP-ABE) by embedding access policy into ciphertext can make data owner to decide which attributes can access ciphertext. It achieves ensuring data confidentiality with realizing fine-grained access control. However, the traditional access policy has some limitations. Compared with other access policies, the circuit-based access policy ABE supports more flexible access control to encrypted data. But there are still many challenges in the existing circuit-based access policy ABE, such as privacy leakage and low efficiency. Motivated by the above, a new circuit-based access policy ABE is proposed. By converting the multi output OR gates in monotonic circuit, the backtracking attacks in circuit access structure is avoided. In order to overcome the low efficiency issued by circuit conversion, outsourcing computing is adopted to Encryption/Decryption algorithms, which makes the computing overhead for data owners and users be decreased and achieve constant level. Security analysis shows that the scheme is secure under the decision bilinear Diffie-Hellman (DBDH) assumption. Numerical results show the proposed scheme has a higher computation efficiency than the other circuit-based schemes. © 2021 IEEE.

关键词： Cloud computing

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A Low-Complexity R-peak Detection Algorithm with Adaptive thresholding for Wearable Devices 25

A Low-Complexity R-peak Detection Algorithm with Adaptive Th...

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25th international conference on pattern recognition (ICPR)

作者： Rodrigues, Tiago Samoutphonh, Sirisack Silva, Hugo Fred, Ana Adv Sport Instruments SA Av Beaumont 5 CH-1012 Lausanne Switzerland IST Inst Super Tecn Av Rovisco Pais 1 P-1049001 Lisbon Portugal IT Inst Telecomunicacoes Av Rovisco Pais 1Torre Norte Piso 10 P-1049001 Lisbon Portugal

ISBN: (纸本)9781728188089

A reliable detection of the R-peaks in an electrocardiogram (ECG) is a fundamental step for further heart rate variability (HRV) analysis, biometric recognition techniques and additional ECG waveform based analysis. In this paper, a novel real-time and low-complexity R-peak detection algorithm is presented for single lead ECG signals. the detection algorithm is divided in two stages. In the first pre-processing stage, the QRS complex is enhanced by taking the double derivative, squaring and moving window integration. In the second, the detection of the R-peak is achieved based on a finite state machine (FSM) approach. the detection threshold is dynamically adapted and follows an exponential decay after each detection, making it suitable for R-peak detection under fast heart rate (HR) and R-wave amplitude changes without additional search back. the proposed algorithm was evaluated in a private single lead ECG database acquired using a FieldWiz wearable device. the database comprises five recordings from four different subjects, recorded during dynamic conditions, namely running, trail running and weightlifting. the raw ECG signals were annotated for the R-peak and the proposed method benchmarked against common QRS detectors. the combined acquisition setup and presented approach resulted in R-peak detection Sensitivity (Se) of 99.77% and Positive Predictive Value of (PPV) of 99.18%, comparable to state of the art real time QRS detectors. Due to its low computational complexity, this method can be implemented in embedded wearable systems, suited for cardiovascular tracking devices in dynamic use cases and R-peak detection.

关键词： Electrocardiograms

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Space-oriented Label Partitioning for Multi-object Tracking 10

Space-oriented Label Partitioning for Multi-object Tracking

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10th international conference on Control, Automation and Information Sciences, ICCAIS 2021

作者： Shim, Changbeom Kim, Du Yong Curtin University School of Electrical Engineering Computing and Mathematical Sciences Perth Australia RMIT University School of Engineering Melbourne Australia

ISBN: (纸本)9781665440295

the problem of tracking multiple objects has been investigated in various research and industrial fields. Among existing methods, random finite set (RFS) solutions such as the generalized labeled multi-Bernoulli (GLMB) filter has provided efficient solutions with solid theoretical justifications. Furthermore, implementations show that the GLMB approach is efficient under challenging scenarios. In this paper, we study an RFS-based method for multi-object tracking (MOT) through a simple data structure for label partitioning. Specifically, grid index structure based techniques for splitting a label space and a label-partitioned GLMB tracker are investigated. We finally evaluate the performance of label partitioning and the GLMB filter via various means such as visualization, execution time, and MOT metrics. © 2021 IEEE.

关键词： Industrial research

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SSE: A Smart Framework for Live Video Streaming based Alerting System 10

SSE: A Smart Framework for Live Video Streaming based Alerti...

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10th international conference on System Modeling and Advancement in Research Trends, SMART 2021

作者： Kumar, Aman Hashmi, Hina Khan, Shueb Ali Kazim Naqvi, S. TMU Faculty of Engineering Computing Sciences UP Moradabad India KSVCEM Dept. of CSE UP Bijnor India Jamia Milliia Islamia FTK-CIT Delhi India

ISBN: (纸本)9781665439701

Automated surveillance is always been a matter of curiosity due to its applications and the freedom through implicit monitoring. A smart implicit monitoring needs to be smarter with un-intervening inference-based classification, decision, and alerting processes. In this same sequence, detection and classification of unusual activities is the utmost curiosity among researchers. the entrance of Artificial Intelligence and the various computing ways (like Machine Learning and Deep Learning methods) of achieving it has been proven the most influential and promising computing revolution in the last decade. AIML-based object detection, segmentation, and identification has proven its vulnerability towards the achievement of these such goals and making computer vision smarter than ever before. In this paper, we are proposing a framework for an intelligent surveillance system based on AIML for video-based live surveillance. the proposed framework will provide a pathway to the intelligent system design for automated monitoring and alerting for unusual events based on detected objects. Basically, it would be a live streaming-based altering system. © 2021 IEEE.

关键词： Monitoring

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Metallic pattern Prediction For Surface Wave Antennas Using Bidirectional Gated Recurrent Unit Neural Network 10

Metallic Pattern Prediction For Surface Wave Antennas Using ...

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10th IEEE-APS Topical conference on Antennas and Propagation in Wireless Communications (APWC) / 22nd international conference on Electromagnetics in Advanced Applications (ICEAA) / USNC-URSI Radio Science Meeting (USNC-URSI RSM)

作者： Yang, Jiashu Tong, Kin-Fai UCL Dept Elect & Elect Engn London WC1E 7JE England

ISBN: (纸本)9781665413886

this work presents a surface wave antenna metallic pattern prediction from electric field in near-field by applying Bidirectional Gated Recurrent Unit neural network prediction model. the metallic pattern of the proposed antenna has been predicted by using Bi-GRU neural network model with prediction accuracy 100% at 34.5G1Iz. Different uniform mark-space-ratios (MSR) of the metallic pattern do not affect the metallic pattern prediction accuracy.

关键词： Surface wave antennas holographic antennas electric field (E-field) prediction recurrent neural network (RNN) bidirectional gated recurrent unit (Bi-GRU)

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A New Electronic Voting System Based on Elliptic Curve in a Cloud computing Environment 12th

A New Electronic Voting System Based on Elliptic Curve in a ...

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12th international conference on Soft computing and pattern recognition, SoCPaR 2020 and 16th international conference on Information Assurance and Security, IAS 2020

作者： Semmouni, Meryem Cherkaoui Azougaghe, Ali Nitaj, Abderrahmane Belkasmi, Mostafa ICES Team ENSIAS Mohammed V University in Rabat Rabat Morocco LMNO University of Caen Normandie Caen France

ISBN: (纸本)9783030736880

the process of democratization is developing and promoting due to electronic voting. However, the increase of frauds and the increasing amount of attacks launched by hackers, gave birth to privacy and authentication problems. Cryptography offers multiple solutions to overcome the sensitive data protection issues in e-voting. In this paper, we study the application of elliptic curve cryptography, and use the homomorphic encryption properties to present a new electronic voting system. Our new scheme is based on the homomorphic cryptosystem EC-ELGAMAL [7], and zero knowledge algorithm of Schnorr algorithm for identification and authentication. © 2021, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Personal computing

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Quantifying the Uncertainty in 3D CT Lung Cancer Images Classification

Quantifying the Uncertainty in 3D CT Lung Cancer Images Clas...

引用

pattern recognition Systems (ICPRS), international conference on

作者： Rahimi Zahari Julie Cox Boguslaw Obara School of Computing Newcastle University Newcastle Upon Tyne UK County Durham and Darlington NHS Foundation Trust UK School of Computing and Biosciences Institute Newcastle University Newcastle Upon Tyne UK

Lung cancer has caused the deaths of millions of people. Early diagnosis is proven vital to increase survival rate. this can be done using computed tomography screening, and when such a system is in place with a computer-aided diagnostic system and deep learning algorithms, it has been proven to be an effective approach. However, most existing systems do not quantify the uncertainty in the prediction. Radiologists should have complete confidence in the predictions during the screening. therefore, to address this issue, we have developed an uncertainty-aware model framework not only to classify lung cancer nodules from 3D computed tomography images, but also to estimate the associated uncertainty in the prediction of the model. We have used the ResNet50 architecture as our base model and, through investigation with different loss functions and the inclusion of class weights, we concluded that the use of the focal loss function shows its superiority over the results of the benchmark model. Two approaches have been adopted to estimate the uncertainty during test time; Monte Carlo dropout and Test Time Augmentation, and from the multiple predictions obtained, three uncertainty metrics have been calculated; entropy, standard deviation, and Bhattacharyya coefficient. Our model achieves the best performance with the F1 score, 83.0 % using the dropout rate, p, 0.5 and Monte Carlo iterations, M, 10. We have also shown that using a threshold value can greatly improve the classification performance even more, but the amount of data to be referred needs to be taken into account.

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Fusion-Based Automated Domain Ontology Construction Method

Fusion-Based Automated Domain Ontology Construction Method

引用

international conference on Computer and Communications (ICCC)

作者： Dongsheng Yang Chang Liu Chinese Academy of Sciences Shenyang Institute of Computing Technology Shenyang China Chinese Academy of Sciences Shenyang Institute of Computing Technology University of Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

Ontology is a crucial component of the Semantic Web, enabling knowledge sharing and reuse across data sources by organizing information into a knowledge network. Currently, domain ontologies are primarily constructed manually by domain experts. However, due to the typically unstructured or semi-structured nature of data sources, combined with large data volumes and the difficulty of content evaluation, the construction process is inefficient. Furthermore, relying on a single data source often fails to ensure the completeness of an ontology. To address these challenges, this paper proposes a fusion-based method for automated domain ontology construction. this approach integrates two types of data sources: relational databases and domain-specific texts (such as research papers and books). the method involves converting relational databases into domain ontology using the RDB2RDF standard, training a domain concept extraction model to identify concepts from texts, and employing pattern matching to extract relationships between concepts for ontology construction. Finally, ontology merging techniques are used to combine data from both sources, generating the final ontology. Using the field of electronic components as a case study, the proposed method was evaluated by domain experts, who confirmed the ontology's comprehensiveness and validated the approach's effectiveness.

关键词： Training Knowledge engineering Semantic Web Soft sensors Computational modeling Electronic components Relational databases Ontologies Standards pattern matching

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Application of Deep Learning for Image Sequence Classification 10

Application of Deep Learning for Image Sequence Classificati...

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10th international conference on System Modeling and Advancement in Research Trends, SMART 2021

作者： Jain, Arpit Gupta, Anurag Sengar, Alok Singh Shukla, Ratnesh Kumar Jain, Abhishek Computer Science Engineering College of Computing Science Information Technology UP Moradabad India Computer Science Engineeringm Roorkee Institute of Technology UK Roorkee India

ISBN: (纸本)9781665439701

Understanding human behavior from videos is a difficult challenge in computer vision. Computers are already pretty competent at recognizing objects in static photos, and such technology is frequently utilized today, but doing so in moving images is far more difficult. Identifying human behavior in films is a key step in creating smarter, gentler robots. Identifying the human activity in motion neural network plays a crucial role. In this study, we proposed a convolutional neural network to identify human activities in video. this algorithm helps in many sectors like anti-terrorists, anti-crime securities, and as well as surveillance, life logging and assistance. © 2021 IEEE.

关键词： Computer vision

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