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检索条件"任意字段=3rd International Conference on Machine Learning and Computer Application, ICMLCA 2022"

共 1863 条记录，以下是1731-1740 订阅

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Ensemble learning to EEG-based Brain computer Interfaces with applications on P300-Spellers

Ensemble Learning to EEG-based Brain Computer Interfaces wit...

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IEEE international conference on Systems, Man, and Cybernetics (SMC)

作者： Barsim, Karim Said Zheng, Wangbo Yang, Bin Stuttgart Univ Inst Signal Proc & Syst Theory Stuttgart Germany

ISBN: (纸本)9781538666500

Brain-computer Interfaces (BCI) are systems in which the electrical activity of an animal brain becomes the main controller of an external electronic device capable of reading and processing electroencephalographic (EEG) signals. One early application of such systems is the attention-based spellers utilizing the P300 visually evoked potential in a framework known as the oddball paradigm. In this paper, we propose novel variants of machine learning model ensembles in addressing the task of P300 detection and attended target recognition in attention-based speller systems. Proposed ensembles adopted Bootstrap aggregation (Bagging) of calibrated Support Vector machines (SVM) as well as data-driven learners. The latter is dominantly represented by Convolutional Neural Networks (CNN) with several variants, including what is referred to as Inception, Xception, and Interleaved Group Convolutions (IGC) modules. The proposed models are evaluated on a publicly available EEG dataset developed specifically for BCI applications and published in public contests, namely the 2rd dataset of the 3rd BCI competitions. The proposed models consistently outperform all previous works on the same dataset and show the highest 5- and 15-trial recognition rates of 76.5% and 98.5%, respectively, for both subjects in the dataset jointly. Additionally, we introduce in this work the first study on inter-subject evaluation under similar training protocols, reaching between 30 %-40 % recognition rates for either subject. We further investigate the effect of reducing the training data on the performance of the proposed models showing possibilities of reduced training time for a target recognition rate.

关键词： Ensemble learning Electroencephalography (EEG) Brain computer Interfaces (BCI) Support Vector machines (SVM) Convolutional Neural Networks (CNN) Interleaved Group Convolutions (IGC)

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Galvanic Skin Response signal based Cognitive Load classification using machine learning classifier

Galvanic Skin Response signal based Cognitive Load classific...

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international conference on Electrical, computer & Telecommunication Engineering (ICECTE)

作者： Md. Toufick E Elahi Iffath Binta Islam Khulna University of Engineering & Technology Khulna Bangladesh

ISBN: (数字)9781728164106

ISBN: (纸本)9781728164113

Cognitive load (CL) classification is an important research issue in the human-computer interaction paradigm. It is evident from recent research that Galvanic Skin Response (GSR) can be used to sense cognitive load. CL analysis is important for understanding the mental growth of the child and the psychology of patients going through the different traumatic situation. Inspired by such a novel application, this model is designed. In this work, a technique has been demonstrated to measure and evaluate the level of human cognitive load for different tasks by collecting GSR from 40 student participants. The students are asked to sit for a test to solve three different tasks - reading comprehension, solving mathematics, and cracking Sudoku. Time-domain features have been extracted from participant's GSR signals while these tasks are being analyzed. Some parameters such as Correlation Dimension (CD), Lempel-Ziv Complexity (LZC), Hurst Exponent (HE) and Shannon Entropy (SE) are used to analyze CL and these are used as features while accomplishing classification. The Level of stress or cognitive load is strongly observed with the one-way ANOVA test and box-whisker plots. Next, several machine learning algorithms are used to classify the various level of cognitive load. Using the 10 fold cross-validation and Naïve Bayes algorithm 91.5% accuracy is obtained.

关键词： Task analysis Feature extraction Stress Time-domain analysis Entropy Correlation Analysis of variance

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Smartphone application identification by convolutional neural network 3rd

Smartphone application identification by convolutional neura...

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3rd international conference on machine learning and Intelligent Communications, MLICOM 2018

作者： Zhao, Shuang Chen, Shuhui College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9783030005566

Mobile traffic has received much attention within the field of network security and management due to the rapid development of mobile networks. Unlike fixed wired workstation traffic, mobile traffic is mostly carried over HTTP/HTTPS, which brings new challenges to traditional traffic identification methods. Although there have been some attempts to address this problem with side-channel traffic information and machine learning, the effectiveness of these methods majorly depends on predefined statistics features. In this paper, we presented an approach based on convolutional neural network without explicit feature extraction process. And owing to no payload inspection requirement, this method also works well even encrypted traffic appears. Six instant message applications are used to verify our approach. The evaluation shows the proposed approach can achieve more than 96% accuracy. Additionally, we also discussed how this approach performed under real-world conditions. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2018.

关键词： Network security

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Sentiment analysis based on deep learning and its application in screening for perinatal depression 3

Sentiment analysis based on deep learning and its applicatio...

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3rd IEEE international conference on Data Science in Cyberspace, DSC 2018

作者： Chen, Yong Zhou, Bin Zhang, Weina Gong, Wenjie Sun, Guangfu College of Computer National University of Defense Technology Changsha China School Business Central South University Changsha China XiangYa School of Public Health Central South University Changsha China College of Electronic Science National University of Defense Technology Changsha China

ISBN: (纸本)9781538642108

In the specific field of sentiment analysis tasks, traditional machine learning models lack effective attention to certain features. To address this issue, we extracted and classified user emotion features based on the LSTM network model and applied them to perinatal depression screening scenarios. We use emoticons as feature extraction and modeling for document-level sentiment analysis in specific regions and achieved good results. The results were basically consistent with the findings of the Edinburgh Postnatal Depression Scale. This method greatly shortens the screening time and reduces the doctor-patient communication costs. It has positive significance for specific areas of sentiment classification tasks and provides references for document-level sentiment analysis. © 2018 IEEE.

关键词： Sentiment analysis

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3rd international Workshop on Semantics, Analytics, Visualization, SAVE-SD 2017 and 4th international Workshop on Semantics, Analytics, Visualization, SAVE-SD 2018

3rd International Workshop on Semantics, Analytics, Visualiz...

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3rd international Workshop on Semantics, Analytics, Visualization, SAVE-SD 2017 and 4th international Workshop on Semantics, Analytics, Visualization, SAVE-SD 2018

ISBN: (纸本)9783030013783

The proceedings contain 12 papers. The special focus in this conference is on Semantics, Analytics, Visualization. The topics include: Investigating Facets to Characterise Citations for Scholars;striving for Semantics of Plant Phenotyping Data;ILastic: Linked Data Generation Workflow and User Interface for iMinds Scholarly Data;About a BUOI: Joint Custody of Persistent Universally Unique Identifiers on the Web, or, Making PIDs More FAIR;extending ScholarlyData with Research Impact Indicators;geographical Trends in Research: A Preliminary Analysis on Authors’ Affiliations;a Web application for Creating and Sharing Visual Bibliographies;optimized machine learning Methods Predict Discourse Segment Type in Biological Research Articles;EVENTS: A Dataset on the History of Top-Prestigious Events in Five computer Science Communities;OSCAR: A Customisable Tool for Free-Text Search over SPARQL Endpoints.

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3rd international conference on machine learning and Intelligent Communications, MLICOM 2018

3rd International Conference on Machine Learning and Intelli...

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3rd international conference on machine learning and Intelligent Communications, MLICOM 2018

ISBN: (纸本)9783030005566

The proceedings contain 66 papers. The special focus in this conference is on machine learning and Intelligent Communications. The topics include: Smartphone application identification by convolutional neural network;joint D2D cooperative relaying and friendly jamming selection for physical layer security;variable tap-length blind equalization for underwater acoustic communication;random sequence generation algorithm for multi-chaotic systems;vulAware: Towards massive-scale vulnerability detection in cyberspace;Optimization in cognitive radio networks with SWIPT-based DF relay;Real-valued orthogonal sequences for ultra-low overhead channel estimation in MIMO-FBMC systems;a novel spectrum allocation scheme in femtocell networks using improved graph theory;distributed channel allocation for wireless mesh networks based on hypergraph interference model;a research of network applications classification based on deep learning;physical violence detection with movement sensors;Joint resource allocation for wireless energy harvesting based on DF relaying;Optimal power splitting of cognitive radio networks with SWIPT-enabled relay;deflection angle detection of the rotor and signal processing for a novel rotational gyroscope;Reusing wireless power transfer for backscatter-assisted cooperation in WPCN;Channel estimation in next generation LEO satellite communicastion systems;A novel approach of semi-blind frequency selection for HF regional emergency maneuver communication;a network coding optimization algorithm for reducing encoding nodes;backscatter-assisted relaying in wireless powered communications network;energy-efficient power allocation scheme based on discrete-rate adaptive modulation in distributed antenna system;real-time drone detection using deep learning approach;a method of balanced sleep scheduling in renewable wireless sensor networks;Pedestrian walking model for floor plan building based on crowdsourcing PDR data.

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Deep learning based attribute classification insider threat detection for data security 3

Deep learning based attribute classification insider threat ...

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3rd IEEE international conference on Data Science in Cyberspace, DSC 2018

作者： Meng, Fanzhi Lou, Fang Fu, Yunsheng Tian, Zhihong Institute of Computer Application China Academy of Engineer Physics Mianyang China Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou China

ISBN: (纸本)9781538642108

With the evolution of network threat, identifying threat from internal is getting more and more difficult. To detect malicious insiders, we move forward a step and propose a novel attribute classification insider threat detection method based on long short term memory recurrent neural networks (LSTM-RNNs). To achieve high detection rate, event aggregator, feature extractor, several attribute classifiers and anomaly calculator are seamlessly integrated into an end-to-end detection framework. Using the CERT insider threat dataset v6.2 and threat detection recall as our performance metric, experimental results validate that the proposed threat detection method greatly outperforms k-Nearest Neighbor, Isolation Forest, Support Vector machine and Principal Component Analysis based threat detection methods. © 2018 IEEE.

关键词： Anomaly detection

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Brain Tumor Segmentation and Parsing on MRIs Using Multiresolution Neural Networks 3rd

Brain Tumor Segmentation and Parsing on MRIs Using Multireso...

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3rd international Workshop on Brain-Lesion (BrainLes) held jointly at the conference on Medical Image Computing for computer Assisted Intervention (MICCAI)

作者： Silvana Castillo, Laura Alexandra Daza, Laura Carlos Rivera, Luis Arbelaez, Pablo Univ Los Andes Dept Biomed Engn Bogota Colombia

ISBN: (纸本)9783319752389;9783319752372

Brain lesion segmentation is a critical application of computer vision to the biomedical image analysis. The difficulty is derived from the great variance between instances, and the high computational cost of processing three dimensional data. We introduce a neural network for brain tumor semantic segmentation that parses their internal structures and is capable of processing volumetric data from multiple MRI modalities simultaneously. As a result, the method is able to learn from small training datasets. We develop an architecture that has four parallel pathways with residual connections. It receives patches from images with different spatial resolutions and analyzes them independently. The results are then combined using fully-connected layers to obtain a semantic segmentation of the brain tumor. We evaluated our method using the 2017 BraTS Challenge dataset, reaching average dice coefficients of 89%, 88% and 86% over the training, validation and test images, respectively.

关键词： Semantic segmentation Brain tumors machine learning Deep learning MRI

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7th Joint international Workshop on Computing and Visualization for Intravascular Imaging and computer Assisted Stenting, CVII-STENT 2018, and the 3rd international Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2018, held in conjunction with the 21th international conference on Medical Imaging and computer-Assisted Intervention, MICCAI 2018

7th Joint International Workshop on Computing and Visualizat...

引用

7th Joint international Workshop on Computing and Visualization for Intravascular Imaging and computer Assisted Stenting, CVII-STENT 2018, and the 3rd international Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2018, held in conjunction with the 21th international conference on Medical Imaging and computer-Assisted Intervention, MICCAI 2018

ISBN: (纸本)9783030013639

The proceedings contain 21 papers. The special focus in this conference is on Computing and Visualization for Intravascular Imaging and computer Assisted Stenting. The topics include: Deep learning retinal vessel segmentation from a single annotated example: An application of cyclic generative adversarial neural networks;an efficient and comprehensive labeling tool for large-scale annotation of fundus images;crowd disagreement about medical images is informative;imperfect segmentation labels: How much do they matter?;crowdsourcing annotation of surgical instruments in videos of cataract surgery;Four-dimensional ASL MR angiography phantoms with noise learned by neural styling;Feature learning based on visual similarity triplets in medical image analysis: A case study of emphysema in chest CT scans;capsule networks against medical imaging data challenges;fully automatic segmentation of coronary arteries based on deep neural network in intravascular ultrasound images;weakly-supervised learning for tool localization in laparoscopic videos;Automated quantification of blood flow velocity from time-resolved CT angiography;Radiology objects in COntext (ROCO): A multimodal image dataset;Improving out-of-sample prediction of quality of MRIQC;multiple device segmentation for fluoroscopic imaging using multi-task learning;segmentation of the aorta using active contours with histogram-based descriptors;layer separation in X-ray angiograms for vessel enhancement with fully convolutional network;Generation of a HER2 breast cancer gold-standard using supervised learning from multiple experts;Deep learning-based detection and segmentation for bvs struts in IVOCT images;towards automatic measurement of type B aortic dissection parameters: Methods, applications and perspective;Prediction of FFR from IVUS images using machine learning.

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An empirical analysis of machine learning classifiers for clinical decision making in Asthma 3rd

An empirical analysis of machine learning classifiers for cl...

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3rd international conference on Cognitive Computing and Information Processing, CCIP 2017

作者： Pooja, M.R. Pushpalatha, M.P. Vidyavardhaka College of Engineering MysuruKarnataka India Sri Jayachamarajendra College of Engineering MysuruKarnataka India

ISBN: (纸本)9789811090585

An empirical analysis of the various machine learning classifiers on Asthma data and their performance assessment are presented. As an integral part of preprocessing the data, feature reduction and outlier detection are carried out, following which classifiers are applied to the same to distinguish asthmatic subjects from those of non-asthmatic ones. In cases where the classifiers perform poorly, their performance is boosted via adaptive classifiers. Feature ranking is adopted to reduce the number of features that are considered for the final process of classification while support vector machine with radial basis function is deployed to eliminate outliers. The performance is evaluated via cross validation and random sampling with variable training sizes. The various performance metrics evaluated for the system including precision, recall, F1-measure and classification accuracy indicate good results. The study can be used as a guideline for the effective application of knowledge discovery techniques on clinical data in order to explore hidden knowledge representations. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Diseases

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