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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1791-1800 订阅

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Robust Common Spatial Patterns based on Bhattacharyya Distance and Gamma Divergence 3

Robust Common Spatial Patterns based on Bhattacharyya Distan...

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3rd international Winter conference on Brain-computer Interface (BCI)

作者： Brandl, Stephanie Mueller, Klaus-Robert Samek, Wojciech Berlin Inst Technol Dept Machine Learning Berlin Germany Korea Univ Dept Brain & Cognit Engn Seoul South Korea Fraunhofer Heinrich Hertz Inst Machine Learning Grp Berlin Germany

ISBN: (纸本)9781479974962

The computation of task-related spatial filters is a prerequisite for a successful application of motor imagery-based Brain-computer Interfaces (BCI). However, in the presence of artifacts, e.g., resulting from eye movements or muscular activity, standard methods such as Common Spatial Patterns (CSP) perform poorly. Recently, a divergence-based spatial filter computation framework has been proposed which enables significantly more robust computation with respect to artifacts by using Beta divergence. In this paper we integrate two additional divergence measures, namely Bhattacharyya distance and Gamma divergence, into the divergence-based CSP framework and evaluate their robustness using simulations and data set IVa from BCI Competition III.

关键词： Divergence divergence Divergence divergence Iva Spatial filters arithmetic Eye Movements communicating sequential processes chip scale packaging phosphocellulose Artifacts Robustness

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Medical diagnosis support and accuracy improvement by application of total scoring from feature selection approach

Medical diagnosis support and accuracy improvement by applic...

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3rd international conference on Innovative Network Systems and applications (iNetSApp) held in conjunction with Federated conference on computer Science and Information Systems (FedCSIS)

作者： Paja, Wieslaw Univ Rzeszow Fac Math & Nat Sci 1 Prof S Pigonia St PL-35310 Rzeszow Poland

ISBN: (纸本)9788360810668

Melanoma is the most deadly form of skin cancer. Early detection and successful treatment of this disease often is possible. The main goal of this paper is to present results of application of feature selection method to find the most important or all important features that characterize melanocytic spots on the skin and in this way defining of a new Total Dermatoscopy Score formula. Thus, it is possible to decrease dimensionality of that problem. Results gathered during research focus on about six from thirteen descriptive attributes which are the most relevant and are stated as core attributes. Based on these attributes a simple total scoring method could be applied to improve prediction (diagnosis) results, additionally also reducing complexity of problem. Results were acquired by application of six different machine learning algorithms and estimated using several evaluation measures.

关键词： cancer feature selection learning (artificial intelligence) medical information systems patient diagnosis skin diagnosis improvement disease detection disease treatment evaluation measures feature selection approach machine learning algorithms medical diagnosis accuracy improvement medical diagnosis support improvement melanocytic spot characterization melanoma prediction improvement skin cancer total dermatoscopy score formula Lesions machine learning algorithms Malignant tumors Pigments Skin Support vector machines

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Enhancing Facial Impression for Video conference 3rd

Enhancing Facial Impression for Video Conference

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3rd international conference on Distributed, Ambient, and Pervasive Interactions (DAPI) held as Part of 17th international conference on Human-computer Interaction (HCI)

作者： Park, Sungyeon Choi, Heeseung Kim, Ig-Jae Univ Sci & Technol HCI Robot Seoul South Korea Korea Inst Sci & Technol Imaging Media Res Ctr Seoul South Korea

ISBN: (纸本)9783319208046;9783319208039

Most people have their preferred impression to be seen by others. Our face warping method can make this real. In this paper, we propose a new technique for automatic transformation of facial impression, for example, look more attractive or baby-face from the original. We build an impression score function, trained by scores from human raters, and the function is used to get displacement vectors for a given face. To preserve the facial identity as much as possible, we constrain the facial deformation range with facial classification. In case of real time application, such as video conference, there are frequent facial expressions variation and position changes. Face tracker is used to cope with this changing situation. Through our experiments, our proposed method can be one of the promising methods for future video conference system.

关键词： Transformation of facial impression Face warping Face tracker machine learning Deformation of face

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Double layer interval graph model: the universal tool for data driven market analysis and forecasting 3

Double layer interval graph model: the universal tool for da...

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3rd international conference on Information Technology and Quantitative Management (ITQM)

作者： Markov, Nikolay V. Dmitriev, Andrey V. Maltseva, Svetlana V. Natl Res Univ Higher Sch Econ Moscow 105187 Russia

This scientific work is dedicated to the development, improvement and application of double layer interval weighted graphs (DLIG) for non-stationary time series forecasting. This model appears to be the universal and easy-to-use tool for modeling the non-stationary time series and forecasting. We observe the double layer version of the model because it's the most representative way in the sense of main idea though you can add several layers more for different purposes. The first layer of the graph is based on empirical fluctuations of system and displays the most potential fluctuations of the system at the time of system training. The second layer of the graph as a superstructure of the first layer displays the degree of modeling error and it's connected with the first layer nodes by edges. The second layer is the way of supervised training implementation with the aim of error minimization. (C) 2015 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： market analysis machine learning DLIG non-linear dynamics forecasting

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The prediction of fatigue using speech as a biosignal 1

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3rd international conference on Statistical Language and Speech Processing, SLSP 2015

作者： Baykaner, Khan Huckvale, Mark Whiteley, Iya Ryumin, Oleg Andreeva, Svetlana Speech Hearing and Phonetic Sciences UCL London United Kingdom Centre for Space Medicine UCL Dorking Surrey United Kingdom Gagarin Cosmonaut Training Centre Star City Russia

ISBN: (数字)9783319257891

ISBN: (纸本)9783319257884

Automatic systems for estimating operator fatigue have application in safety-critical environments. We develop and evaluate a system to detect fatigue from speech recordings collected from speakers kept awake over a 60-hour period. A binary classification system (fatigued/not-fatigued) based on time spent awake showed good discrimination, with 80 % unweighted accuracy using raw features, and 90 % with speaker-normalized features. We describe the data collection, feature analysis, machine learning and cross-validation used in the study. Results are promising for real-world applications in domains such as aerospace, transportation and mining where operators are in regular verbal communication as part of their normal working activities. © Springer international Publishing Switzerland 2015.

关键词： learning systems

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Multiple Outputs Programmable Integrated Circuits (MOPICs) Microcontroller Trainer for Educational applications 3

Multiple Outputs Programmable Integrated Circuits (MOPICs) M...

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3rd international conference on Artificial Intelligence, Modelling and Simulation (AIMS)

作者： Burhan, Ibrahim Azman, Ahmad Aftas Talib, Saharuddin Ab Aziz, Ahmad Azlan Univ Newcastle Fac Engn & Built Environm Sch Engn Mech Engn Callaghan NSW 2308 Australia Univ Technol Mara UiTM Fac Elect Engn Shah Alam Selangor Malaysia Sultan Salahuddin Abdul Aziz Shah Polytech Dept Elect Engn Shah Alam Selangor Malaysia

ISBN: (纸本)9781467386753

Multiple Outputs Programmable Integrated Circuits (MOPICs) trainer for educational applications is fabricated based on unsatisfactory usage of PIC simulation which is not able to achieve the learning outcome especially in enhancing leaner's hands-on skill through circuit designing, connection and trouble shooting. Current PIC programming is done through computer simulation, cause the leaners facing difficulty in understanding the wiring connection between the input component, PIC microcontroller and output components. The fabrication of MOPICs trainer was designed and improved by adding more output components, cost effective and user friendly. Moreover, MOPICs trainer are embedded with input component such as normally open push buttons, and 5 difference output components such as sevensegment display, servomotor, LEDs, LCD display, and 12VDC motor. Newly fabricated MOPIC trainer can be programme and re-programme for numerous tasks. Based on respondent questionnaire, it is show that on average of 92.59% agreed that newly developed MOPICs trainer is user friendly, safe and attractive to be used while 94.64% respondent agreed that there are enhancements in theoretical knowledge aspect and hands-on skill implementation in their learning process.

关键词： Multiple Output Programmable Integrated Circuits (MOPICs) microcontroller educational application theoritical knowledge hands-on implementation

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3rd international conference on Statistical Language and Speech Processing, SLSP 2015

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3rd international conference on Statistical Language and Speech Processing, SLSP 2015

ISBN: (纸本)9783319257884

The proceedings contain 27 papers. The special focus in this conference is on Statistical Language and Speech Processing. The topics include: Towards two-way interaction with reading machines;the prediction of fatigue using speech as a biosignal;supertagging for a statistical HPSG parser for spanish;residual-based excitation with continuous F0 modeling in HMM-based speech synthesis;discourse particles in french;effects of evolutionary linguistics in text classification;evaluation of the impact of corpus phonetic alignment on the HMM-based speech synthesis quality;decoding distributed tree structures;combining continuous word representation and prosodic features for ASR error prediction;semi-extractive multi-document summarization via submodular functions;a comparison of human and machine estimation of speaker age;acoustical frame rate and pronunciation variant statistics;the influence of boundary depth on phrase-final lengthening in russian;automatic detection of voice disorders;semantic features for dialogue act recognition;conversational telephone speech recognition for lithuanian;long-term statistical feature extraction from speech signal and its application in emotion recognition;rhythm-based syllabic stress learning without labelled data;unsupervised and user feedback based lexicon adaptation for foreign names and acronyms;combining lexical and prosodic features for automatic detection of sentence modality in french;corpus based methods for learning models of metaphor in modern greek;probabilistic speaker pronunciation adaptation for spontaneous speech synthesis using linguistic features;weakly supervised discriminative training of linear models for natural language processing and merging of native and non-native speech for low-resource accented ASR.

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Robust common spatial patterns based on Bhattacharyya distance and Gamma divergence

Robust common spatial patterns based on Bhattacharyya distan...

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international Winter Workshop on Brain-computer Interface (BCI)

作者： Stephanie Brandl Klaus-Robert Müller Wojciech Samek Department of Machine Learning Berlin Institute of Technology Berlin Germany Technische Universitat Berlin Berlin Berlin DE Machine Learning Group Fraunhofer Heinrich Hertz Institute Berlin Germany

关键词： Robustness Linear programming Pollution measurement Electroencephalography Probability distribution Feature extraction Standards

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An Efficient learning Scheme for Extreme learning machine and Its application

An Efficient Learning Scheme for Extreme Learning Machine an...

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3rd international Multi-conference on computer, Electrical, Electronics and Mechanical Engineering (IMCEEME’15);3rd international Multi-conference on Agricultural Chemical, Biological and Ecosystems (IMACBE’15)

作者： Kheon-Hee Lee Miso Jang Keun Park Dong-Chul Park Yong-Mu Jeong Soo-Young Min Department of Electronics Engineering Myong Ji University Software Device Research Center Korea Electronics Technology Institute

An efficient learning scheme for extreme learning machine(ELM) is proposed in this paper. One of the main obstacles in practical use of convolutional neural network(CNN) is their computational complexity in its training stage. The extreme learning machine can be a promising tool for solving the computational complexity in CNNs. The weights between input layer and hidden layer are randomly generated in ELM and these weights can be tuned for more efficient learning. This paper also proposes a variation of ELM, called Average ELM, that replaces input layer's random weights with predetermined weights. This paper shows a feasibility study in tuning these weights and applying extreme learning machine to CNNs for faster training with some initial experimental results. The proposed method can reduce the size of target network without sacrificing the performance of neural network. Experiments on several databases show that the proposed training method can reduce the training time while preserving the classification accuracy.

关键词： data classification. extreme learning machine.neural networks.random weights

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MALMOS: machine learning-Based Mobile Offloading Scheduler with Online Training

MALMOS: Machine Learning-Based Mobile Offloading Scheduler w...

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IEEE international conference on Mobile Cloud Computing, Services, and Engineering (MobileCloud)

作者： Heungsik Eom Renato Figueiredo Huaqian Cai Ying Zhang Gang Huang Electrical and Computer Engineering University of Florida Gainesville Florida USA Laboratory of High Confidence Software Technologies Peking University Beijing China

This paper proposes and evaluates MALMOS, a novel framework for mobile offloading scheduling based on online machine learning techniques. In contrast to previous works, which rely on application-dependent parameters or predefined static scheduling policies, MALMOS provides an online training mechanism for the machine learning-based runtime scheduler such that it supports a flexible policy that dynamically adapts scheduling decisions based on the observation of previous offloading decisions and their correctness. To demonstrate its practical applicability, we integrated MALMOS with an existing Java-based, offloading-capable code recapturing framework, Partner. Using this integration, we performed quantitative experiments to evaluate the performance and cost for three machine learning algorithms: instance-based learning, perception, and naive Bays, with respect to classifier training time, classification time, and scheduling accuracy. Particularly, we examined the adaptability of MALMOS to various network conditions and computing capabilities of remote resources by comparing the scheduling accuracy with two static scheduling cases: threshold-based and linear equation-based scheduling policies. Our evaluation uses an Android-based prototype for experiments, and considers benchmarks with different computation/communication characteristics, and different computing capabilities of remote resources. The evaluation shows that MALMOS achieves 10.9%~40.5% higher scheduling accuracy than two static scheduling policies.

关键词： Training Mobile communication Runtime Processor scheduling Accuracy Dynamic scheduling

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