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检索条件"任意字段=International Conference on Pattern Analysis and Intelligence Robotics"

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Classification & Detection of Epilepsy Using IEEG Application

Classification & Detection of Epilepsy Using IEEG Applicatio...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： R. Kishore Kanna Kapil Joshi Kamreed Udham Singh Ambali Pacholi Mathar Mohideen Nagoor Thangam X.S. Blessing NimaSajai Department of Biomedical Engineering Jerusalem College of Engineering (Autonomous) Chennai India Department of CSE Uttaranchal Institute of Technology Uttaranchal University Dehradun India School of Computing Graphic Era Hill University Adjunct Professor Graphic Era Deemed to be University Dehradun Uttarakhand School of Nursing Science ITM University Gwalior (MP) India Department of Nursing Faculty of Applied Medical Sciences University of Tabuk Saudi Arabia College of Nursing Khamis MushaytKing Khalid University Kingdom Of Saudi Arabia

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

More than only cognitive loss and impairment, epileptic seizures may seriously harm one’s intellect. Early seizure detection is essential to minimize the harm caused by their possessions. Presently, medical professionals use optical testing of electroencephalographic (EEG) data for project labeling, which may be a labor-intensive and delayed procedure. By resolving EEG data using the best spatial convolutional interconnected system-long temporary thought (1D CNNLSTM) model, this study offers a revolutionary pattern for always identifying seizures. Before the raw EEG dossier is augmented into a 1D CNN that pulls appearance data from the dossier, it is preprocessed and standardized. Hence, worldly faces are extracted using Long Short-Term Memory (LSTM) layers, which are then combined into various suitably linked coatings for capture recognition. The projected model’s representation was evaluated using the UCI epileptic capture acknowledgment dataset. For the twofold and five-class taking acknowledgment tasks, respectively, the outcomes show an exceptional acknowledgment veracity of 99.39 % and 82.00 %. The predicted model achieved better act when compared to existing machine intelligence setups and additional deep education models. These rulings highlight the promise of the proposed method for accurately diagnosing seizures in patients who are experiencing muscular spasms and acknowledge the potential for improved impartial outcomes.

关键词： Optical losses Optical interconnections Predictive models Brain modeling Electroencephalography Task analysis Spatial resolution

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Autonomous Reactive LiDAR-based Mapping for Powerline Inspection

Autonomous Reactive LiDAR-based Mapping for Powerline Inspec...

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international conference on Unmanned Aircraft Systems (ICUAS)

作者： J. Paneque V. Valseca J. R. Martí nez-de Dios A. Ollero GRVC Robotics Lab Sevilla Universidad de Sevilla Spain

ISBN: (数字)9781665405935

ISBN: (纸本)9781665405942

There is a strong demand in the automation of powerline inspection, which is currently performed in a two-stage process: 1) data collection by an aerial vehicle equipped with LiDARs, cameras, and other sensors, and 2) offline data analysis using processing and artificial intelligence algorithms. This procedure is very inefficient and in many cases requires repeating the flights if during the offline analysis the collected data is found to be of insufficient quality for the inspection. This paper proposes a reactive quadrotor-based online powerline inspection system. The proposed method: i) builds online an onboard an accurate preliminary 3D map of the environment, ii) performs an online analysis of the quality of the inspection data being obtained, and iii) in case the data has insufficient quality, commands the quadrotor to stop moving so that more data of that part of the environment can be integrated to increase the resolution and precision until the required quality is fulfilled. Hence, the proposed system maximizes the area inspected ensuring that the gathered data fulfils the specified quality metrics. The proposed system has been implemented onboard and aerial robot and validated in powerline inspection experiments conducted in environments with different types conditions and vegetation.

关键词： Measurement Laser radar Three-dimensional displays Vegetation mapping Inspection Software Robustness

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Artificial intelligence Based Hierarchical Classification of Frontotemporal Dementia

Artificial Intelligence Based Hierarchical Classification of...

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Annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Km Poonam Rajlakshmi Guha Partha P Chakrabarti Centre of Excellence in AI IIT Kharagpur West Bengal India Centre for the Science of Happiness IIT Kharagpur West Bengal India Department of Computer Science & Engineering IIT Kharagpur West Bengal India

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Frontotemporal dementia (FTD) is a typical kind of presenile dementia with three main subtypes: behavioral-variant FTD (bvFTD), non-fluent variant primary progressive aphasia (nfvPPA), and semantic variant primary progressive aphasia (svPPA). Our aim is to classify brain images of each subject into one of the spectrums of the FTD in a hierarchical order by applying data-driven techniques. Specifically, we took 300 subjects (30 bvFTD, 41 svPPA, 25 nfvPPA, 80 Alzheimer’s Disease, and 124 cognitively normal) from the Frontotemporal Lobar Degeneration Neuroimaging Initiative archive to validate our approach. The cortical and subcortical measurements, e.g., cortical thickness and volumetric segmentation, were extracted from MRI images for the experiment. Our proposed model yielded classification accuracy of 87.42%, 83.23%, and 82.44% with Support Vector Machine (SVM), Linear Discriminant analysis, and Naive Bayes methods, respectively, for five classes. We observed a significant improvement over the flat multi-class model, which has an accuracy of 82.80%, 81.27%, and 80.45%, respectively, for five classes. We also compared our proposed approach with a Convolutional Neural Networks model and ensemble with multi-layer perception as well. The results showed that the hierarchical approach performs almost equally well here as in the SVM model. Moreover, we also tested our hierarchical approach for three classes (CN, Non-FTD, FTD) to compare with state-of-the-art methods and obtained better *** relevance— This study provides a detailed understanding of the heterogeneity within the FTD subtypes; clinicians can better tailor diagnostic and treatment strategies to match each subtype’s specific characteristics and progression patterns.

关键词： Support vector machines Neuroimaging Accuracy Volume measurement Semantics Aphasia Resonance Naive Bayes methods pattern matching Thickness measurement

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Recognizing boundaries of online professional learning communities in an automated discourse analysis approach

Recognizing boundaries of online professional learning commu...

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Intelligent Education and Intelligent Research (IEIR), international conference on

作者： Si Zhang YuTing Zhang Xinyue He TongYu Guo Yiyao Wang Faculty of Artificial Intelligence in Education Central China Normal University Wuhan China

ISBN: (纸本)9798350345636

Recognizing boundaries of online professional learning communities can help to provide teachers with a meaningful online learning environment that improves their training performance. This study proposed an automated discourse analysis approach for recognizing boundaries of the online learning communities, that combines both Topic Modelling approach (Latent Dirichlet Allocation) and Social Network analysis. The study examined online discourse data of 1843 teachers participating in an online training program. The findings revealed that teachers mainly responded to others’ posts and the pattern of teachers’ response could be mainly divided into four types. The semantic network formed by discourse unit was high-density with low average network distance and high degree centrality, and the cohesion parameter of the semantic network was relatively stable during the whole process of online discourse. The findings of the study also can provide insights into creating online learning communities and teacher education.

关键词： Training Analytical models Social networking (online) Atmospheric measurements Semantics Particle measurements Behavioral sciences

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Crowdsensed Data-oriented Distributed and Secure Spatial Query Scheme

Crowdsensed Data-oriented Distributed and Secure Spatial Que...

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IEEE international conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Yuxi Li Fucai Zhou Jingjing Chen Dong Ji School of Computer Science and Engineering Northeastern University Shenyang China Software College Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Focusing on the privacy concerns and leakage abuse of sensory data collection in mobile crowdsensing (MCS) environment, we propose a crowdsensed data-oriented distributed and secure spatial query scheme while ensuring data privacy as long as query patterns. To cater to the demands of real-world MCS workloads, we designed a distributed multi-layer architecture and leveraged distributed hash functions(DHT) and broadcast encryption(BE) to achieve load-balancing and enforce access control. Our scheme incorporates a recently developed cryptographic tool–function secret sharing (FSS) to safeguard the query pattern and sensory data from potential compromises at the server *** analysis demonstrates that our scheme achieves affordable query complexity while satisfying adaptive $\mathcal{L}$-semantic security. Encouraging experimental results substantiate the efficacy of our scheme, the growth rates of query cost diminishes as the number of records and participants increases. These findings emphasize the suitability of our scheme for crowdsensing applications with fine-grained access control requirements and establish it as an efficient cryptographic tool that holds promise for diverse MCS applications.

关键词： Access control Data privacy Costs Crowdsensing Focusing Data collection Complexity theory

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5th international conference on Intelligent Computing and Applications, ICICA 2019

5th International Conference on Intelligent Computing and Ap...

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5th international conference on Intelligent Computing and Applications, ICICA 2019

ISBN: (纸本)9789811555657

The proceedings contain 71 papers. The special focus in this conference is on Intelligent Computing and Applications. The topics include: Performance Estimation and analysis Over the Supervised Learning Approaches for Motor Imagery EEG Signals Classification;fully Automated Digital Mammogram Segmentation;empirical Study of Computational intelligence Approaches for the Early Detection of Autism Spectrum Disorder;Intelligent Monitoring of Bearings Using Node MCU Module;image Denoising Using Various Image Enhancement Techniques;energy Consumption analysis and Proposed Power-Aware Scheduling Algorithm in Cloud Computing;relationship Between Community Structure and Clustering Coefficient;digital Image Forensics-Image Verification Techniques;energy Efficient Data Centre Selection Using Service Broker Policy;using Automated Predictive Analytics in an Online Shopping Ecosystem;design and Development of Home Automation System;performance analysis Study of Stochastic Computing Based Neuron;Scheduling of Parallel Tasks in Cloud Environment Using DAG MODEL;GREENIE—Smart Home with Smart Power Transmission;ANAVI: Advanced Navigation Assistance for Visually Impaired;THIRD EYE—Shopfloor Data Processing and Visualization Using Image Recognition;A Hybrid Search Group Algorithm and pattern Search Optimized PIDA Controller for Automatic Generation Control of Interconnected Power System;Fractional Order PID Controlled PV Fed Quadratic Boost Converter TZ Source Inverter Fed Permanent Magnet Brushless Motor Drive;co-ordinate Measurement of Roll-Cage Using Digital Image Processing;peformance analysis of Joysticks Used in Infotainment Control System in Automobiles;Probabilistic Principal Component analysis (PPCA) Based Dimensionality Reduction and Deep Learning for Cancer Classification;blockchain Technology for Data Sharing in Decentralized Storage System;Understanding Concepts of Blockchain Technology for Building the DApps;chennai Water Crisis—Data analysis.

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Sinc-based Multiplication-Convolution Network for Equipment Intelligent Edge Diagnosis under Small Samples

Sinc-based Multiplication-Convolution Network for Equipment ...

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Sensing, Measurement & Data Analytics in the era of Artificial intelligence (ICSMD), international conference on

作者： Rui Liu Xiaoxi Ding Qihang Wu Hao Xiang Huaping Tan Yimin Shao State Key Laboratory of Vehicle NVH and Safety Technology College of Mechanical and Vehicle Engineering Chongqing University Chongqing China The State Key Lab of Mechanical Transmissions Chongqing University Chongqing China College of Mechanical and Vehicle Engineering Chongqing University Chongqing China

Data-driven intelligent diagnosis models need massive monitoring data to train themselves for desired performance. However, in many engineering scenarios, collecting fault data is often expensive and time-consuming, which leads to few-shot learning becoming a valuable research hotspot for intelligent diagnosis. Inspired by mode characteristics and feature enhancement learning, this study propose a Sinc-based multiplication-convolution network (SincMCN) for intelligent fault diagnosis under small samples. It works in frequency domain, and consists of only three layers, including a feature separator, a feature extractor and a classifier. In the feature separator, a series of Sinc-based multiplication filtering kernels (SincMFKs) are designed for improving the utilization of fault information of spectrum samples. The products between SincMFKs and spectrum samples are stacked into activated mode spectrum images (AMSIs) with rich fault-related features retained. Since AMSIs are concise enough, this study employs only a 2D convolutional layer and a fully connected layer as the feature extractor and the classifier for achieving a fast and precise pattern recognition. Experimental results show SincMCN has better diagnosis accuracy and stronger potentials for few-shot diagnosis compared other cutting-edge models. Specially, analytic filtering kernels not only cut down the model parameters for edge diagnosis and provide powerful application potentials and engineering value for online monitoring of rotating machinery.

关键词： Fault diagnosis Training Filtering Particle separators Frequency-domain analysis Feature extraction pattern recognition

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Exploring Dynamic Selection of Branch Expansion Orders for Code Generation 59

Exploring Dynamic Selection of Branch Expansion Orders for C...

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Joint conference of 59th Annual Meeting of the Association-for-Computational-Linguistics (ACL) / 11th international Joint conference on Natural Language Processing (IJCNLP) / 6th Workshop on Representation Learning for NLP (RepL4NLP)

作者： Jiang, Hui Zhou, Chulun Meng, Fandong Zhang, Biao Zhou, Jie Huang, Degen Wu, Qingqiang Su, Jinsong Xiamen Univ Sch Informat Xiamen Peoples R China Xiamen Univ Inst Artificial Intelligence Xiamen Peoples R China Tencent Inc WeChat AI Pattern Recognit Ctr Shenzhen Peoples R China Univ Edinburgh Sch Informat Edinburgh Midlothian Scotland Dalian Univ Technol Dalian Peoples R China

ISBN: (纸本)9781954085527

Due to the great potential in facilitating software development, code generation has attracted increasing attention recently. Generally, dominant models are Seq2Tree models, which convert the input natural language description into a sequence of tree-construction actions corresponding to the pre-order traversal of an Abstract Syntax Tree (AST). However, such a traversal order may not be suitable for handling all multi-branch nodes. In this paper, we propose to equip the Seq2Tree model with a context-based Branch Selector, which is able to dynamically determine optimal expansion orders of branches for multibranch nodes. Particularly, since the selection of expansion orders is a non-differentiable multi-step operation, we optimize the selector through reinforcement learning, and formulate the reward function as the difference of model losses obtained through different expansion orders. Experimental results and in-depth analysis on several commonly-used datasets demonstrate the effectiveness and generality of our approach.

关键词： Expansion

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A Bibliometric and Co-occurrence analysis of Speech Processing Literature Published from the Year 2015 to mid of June 2021

A Bibliometric and Co-occurrence Analysis of Speech Processi...

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Proceedings of the international conference on Data Science, Machine Learning and Artificial intelligence

作者： Nabanita Das Neelamadhab Padhy School of Computer Engineering (SOCE) GIET University Gunupur India

ISBN: (纸本)9781450387637

Speech processing technology will continue to be a fast-increasing area of signal processing for many years to come, based on present trends. Research in this field is useful in a variety of fields, including voice identification, interactive voice systems, emotion recognition, call center automation, virtual assistants, and robotics. Artificial intelligence, machine learning, and signal processing have all made significant strides recently, making this viable. The goal was for authors/co-authors of documents to evaluate speech processing in Scopus in terms of research trends and leading countries. The search syntax was based on the title of the article. Our article summarizes the research that has been done on this topic over the last five years. This study looked at 5,082 "speech processing" records from January 1, 2015, to June 14, 2021, which were collected from the Scopus database using the keywords "Speech Processing" and "keyword in the title (item)" and then evaluated with VOSviewer software. Several countries are ranked substantially higher in Scopus in terms of journal coverage, according to this analysis. The United States, China, Japan, India, and the United Kingdom are the major contributors. The interpretation of the results is determined by the search phrase used and the database or information system under investigation. According to this survey, in the area of speech processing, 73 percent overall research papers published in the last five years are conference papers. The studied co-occurrence relationship between keyword in the title clearly demonstrates that this topic of research is steadily being explored. This analysis may inspire and motivates the research community who are working in this discipline.

关键词： Bibliometric analysis

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Adversarial Black-Box Attacks on Vision-based Deep Reinforcement Learning Agents

Adversarial Black-Box Attacks on Vision-based Deep Reinforce...

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IEEE international conference on intelligence and Safety for robotics (ISR)

作者： Atanas Tanev Svetlana Pavlitskaya Joan Sigloch Arne Roennau Ruediger Dillmann J. Marius Zollner FZI Research Center for Information Technology Karslruhe Germany Karlsruhe Institute of Technology (KIT) Germany

Deep reinforcement learning (DRL) has had a profound impact in the field of robotic learning, especially in vision-based end-to-end applications. To ensure the robustness and stability of such robotic systems, their vulnerability to possible adversarial attacks must be explored. In this paper we demonstrate the first realistic black-box attack on vision-based DRL systems by adopting the concept of adversarial patches. Agents trained to perform object grasping based only on visual input are manipulated through their observation space. Different patch sizes and positions for attacks targeting DRL systems in simulation are evaluated, which prove to be decisive factors that greatly influence the effectiveness of adversarial patch attacks. Despite the fact that evaluated adversarial patches take up less than 2% of the images observed, the attacked DRL systems are heavily affected and show performance drop of up to 99 %. Our experiments and results in the simulation pave the way for more realistic adversarial attacks on DRL agents.

关键词： Visualization conferences Reinforcement learning Grasping Stability analysis Robustness Safety

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