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A Survey on Deep learning in Electromyographic Signal Analysis 15th

A Survey on Deep Learning in Electromyographic Signal Analys...

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15th international conference on intelligent Computing, ICIC 2019

作者： Buongiorno, Domenico Cascarano, Giacomo Donato Brunetti, Antonio De Feudis, Irio Bevilacqua, Vitoantonio Department of Electrical and Information Engineering Polytechnic University of Bari Bari Italy Apulian Bioengineering s.r.l. Via delle Violette n814 ModugnoBA Italy

ISBN: (纸本)9783030267650

In the recent past Deep learning (DL) has been used to develop intelligent systems that perform surprisingly well in a large variety of tasks, e.g. image recognition, machine translation, and self-driving cars. the huge improvement of the elaboration hardware and the growing need of big data processing have boosted the DL research in several fields. Recently, physiological signal processing has taking advantage of deep learning as well. In particular, the number of studies concerning the analysis of electromyographic (EMG) signals with DL methods is exponentially raising. this phenomenon is mainly explained by both the existing limitation of the myoelectric controlled prostheses and the recent publication of big datasets of EMG recordings, e.g. Ninapro. Such increasing trend motivated us to search and review recent papers that focus on the processing of EMG signals with DL methods. A comprehensive literature search of papers published between January 2014 and March 2019 was performed referring to the Scopus database. After a full text analysis, 65 papers were selected for the review. the bibliometric research shows four distinct clusters focused on different applications: Hand Gesture Classification;Speech and Emotion Classification;Sleep Stage Classification;Other Applications. As expected, the review process revealed that most of the papers related to DL and EMG signal processing concerns the hand gesture classification, and the convolutional neural network is the most used technique. © 2019, Springer Nature Switzerland AG.

关键词： Recurrent neural networks

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Smoothed data density histogram on self-organizing map and its application to cluster analysis

Smoothed data density histogram on self-organizing map and i...

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international Symposium on Soft Computing and intelligent Systems (SCIS)

作者： Yuriko Tsunoda Hideaki Kawano Hiroshi Maeda Graduate School of Engineering Faculty of Engineering Kyushu Institute of Technology Japan

this paper proposes an automatic clustering method using self-organizing feature maps (SOM). there are a lot of studies on the automatic clustering. Among them, SOM-based clustering methods are attracted in terms of visual understandabilities. In general SOM, each weight vector after learning covers a subspace in input space. In the SOM-based clustering, a histogram, i.e. a density distribution of data, is constructed from the frequency of data existing in each subspace. In most situations, the histogram obtained from the learnt SOM involves more excessive peaks and valleys than we envisioned by the true number of clusters. therefore, it is difficult to assign the data to clusters from the visual inspections of histogram. In this study, to eliminate the unnecessary extremums from histogram and make cluster analyses easier, a method to smooth the histogram is proposed. In the proposed method, the histogram is edited by considering learnt weight vectors and neighborhood characteristics. As a result, the edited histogram is enough smoothed to understand the relationships between the agglomeration of data and clusters. In addition, an automatic clustering scheme is proposed on the basis of the edited histogram. the effectiveness and the validity of the proposed method for the automatic clustering are examined for several artificially generated data.

关键词： Histograms Vectors Clustering methods Topology Distributed databases Visualization Equations

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Competitive Self-Training technique for sentiment analysis in mass social media

Competitive Self-Training technique for sentiment analysis i...

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international Symposium on Soft Computing and intelligent Systems (SCIS)

作者： Sola Hong Jaedong Lee Jee-Hyong Lee Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Republic of Korea

ISBN: (纸本)9781479959563

this paper aims to analyze user's emotion automatically by analyzing Twitter using "data without sentiment labels", not only "data with sentiment labels", to increase accuracy of sentiment analysis through an improved Self-Training, one of Semi-supervised learning techniques. Self-Training has a weak point that a classification mistake can reinforce itself. Self-Training iteratively modifies the model based on the output of the model. thus, if the model generates wrong output, the model can be wrongly modified. For alleviate this weak point, we propose a competitive Self-Training technique. We create three models based on the output of the model and choose the best. three models are created by binary mixture perspectives: the threshold, the same number, and the maximum number for updates. We repeat step that creating model and choosing a best model highest to get F-measure. Finally, we can improve the performance of sentiment analysis model.

关键词： data models Sentiment analysis Analytical models Support vector machines Accuracy Twitter Training

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Graph-based clustering based on cutting sets

Graph-based clustering based on cutting sets

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IEEE international conference on intelligent engineering Systems (INES)

作者： K. Buza P. B. Kis A. Buza Information Systems and Machine Learning Laboratory University of Hildesheim Hildesheim Germany Department of Mathematics College of Dunaujvaros Dunaujvaros Hungary Department of IT College of Dunaujvaros Dunaujvaros Hungary

One of the most prominent challenges in data mining is the clustering of databases containing many categorical attributes. Representation of such data in continuous, Euclidean space usually does not reflect the true segments of data. As a crucial consequence, clustering algorithms working in continuous, Euclidean space may produce segmentations of poor quality. An alternative direction explores graph-based representation of data. In this paper, we show that graph-based data representation is well suitable for the case of categorical attributes. In particular, we offer the following contributions: i) we propose and analyze a flexible graph-based genetic clustering algorithm, where the ideal clusters can be characterized using external cluster quality functions, called kernels, ii) we study kernels, and define the crucial property of effective kernels, iii) we introduce a framework for distributed data-oriented graph clustering computations. In contrast of the complexity of our problem, which turns out to be NP-hard in our analysis, experiments show that in case of well clusterable data, our algorithm has attractive scalability properties. We also perform experiments on real medical data that provides us with further evidence about the practical applicability of our approach.

关键词： Kernel Clustering algorithms Genetic algorithms databases Servers Algorithm design and analysis Complexity theory

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Novel Naive Bayes based on Attribute Weighting in Kernel Density Estimation

Novel Naive Bayes based on Attribute Weighting in Kernel Den...

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international Symposium on Soft Computing and intelligent Systems (SCIS)

作者： Zhong-Liang Xiang Xiang-Ru Yu Alex Wong Ming Hui Dae-Ki Kang Weifang University of Science & Technology Weifang Shandong China Division of Computer and Information Engineering Dongseo University Busan South Korea Dongseo University Busan KR

Naive Bayes (NB) learning is more popular, faster and effective supervised learning method to handle the labeled datasets especially in which have some noises, NB learning also has well performance. However, the conditional independent assumption of NB learning imposes some restriction on the property of handling data of real world. Some researchers proposed lots of methods to relax NB assumption, those methods also include attribute weighting, kernel density estimating. In this paper, we propose a novel approach called NB Based on Attribute Weighting in Kernel Density Estimation (NBAWKDE) to improve the NB learning classification ability via combining kernel density estimation and attribute weighting based on conditional mutual information. Our method makes the weights embedded in kernel have the relatively interpretable meaning, it is flexible that we also can choice different metrics and methods to measure the weights based on our attribute weighting in kernel density estimation framework.

关键词： Niobium Kernel Estimation Mutual information Weight measurement Training Educational institutions

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On a lip print recognition by the pattern kernel with multi-resolution architecture

On a lip print recognition by the pattern kernel with multi-...

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4th international conference on intelligent data engineering and automated learning, ideal 2003

作者： Kim, Jin Ok Baik, Kyong Seok Chung, Chin Hyun School of Information and Communication Engineering Sungkyunkwan University 300 Chunchun-dong Jangan-gu SuwonKyunggi-do440-746 Korea Republic of Department of Information and Control Engineering Kwangwoon University 447-1 Wolgye-dong Nowon-gu Seoul139-701 Korea Republic of

ISBN: (纸本)354040550X

Biometric systems are forms of technology that use unique human physical characteristics to automatically identify a person. they have sensors to pick up some physical characteristics, convert them into digital patterns, and compare them with patterns stored for individual identification. However lip-print recognition has been less developed than recognition of other human physical attributes such as the fingerprint, voice patterns, retinal blood vessel patterns, or the face. the lip print recognition by a CCD camera has the merit of being linked with other recognition systems such as the retinal/iris eye and the face. A new method using multi-resolution architecture is proposed to recognize a lip print from the pattern kernels. A set of pattern kernels is a function of some local lip print masks. this function converts the information from a lip print into digital data. Recognition in the multi-resolution system is more reliable than recognition in the single-resolution system. the multiresolution architecture allows us to reduce the false recognition rate from 15% to 4. 7%. this paper shows that a lip print is sufficiently used by the measurements of biometric systems. © Springer-Verlag Berlin Heidelberg 2003.

关键词： Architecture

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Embedded fuzzy classifier for detection and classification of preseizure state using real EEG data 15

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15th international conference on Biomedical engineering, ICBME 2013

作者： Qidwai, Uvais Malik, Aamir Saeed Shakir, Mohamed Computer Engineering Department Qatar University Qatar Neuro-Signal Processing Group Centre for Intelligent Signal and Imaging Research Universiti Teknologi PETRONAS TronohPerak Malaysia

ISBN: (纸本)9783319029122

A Classification technique using Fuzzy Logic Inference System to identify and predict the partial seizure from the epileptic EEG data along with preliminary brain conditions in different scenarios is presented in this paper. this detection system can produce warning signals for epileptic seizures. Electroencephalography (EEG) plays an important role, especially EEG based health diagnosis of brain disorder. However, the common clinical methods are insufficient when it comes to design an automated module to detect and predict partial seizure for epileptic patients. If the detection system is to be designed for ubiquitous applications, the system becomes even more complex if the patient is not confined to clinical environment when the device is monitoring continuously while the patient is involved in daily activities. therefore, the work presented here includes embedded hardware system that works with classification algorithm on real EEG signals, in a ubiquitous setting. the performance of the system is shown under various conditions of daily activities. In order to make all this in a ubiquitous form factor, the algorithm for classification and detection of the pre-seizure conditions should be tremendously simple for processing the signal in a low cost ubiquitous microcontroller. this has been achieved in this work through the use of Fuzzy Classifiers based on the lookup table to empower system simplicity. the algorithm also utilizes certain statistical features from the EEG signal that are used as features to the classifier logic. While the clinical testing of the device is still awaited, various scenarios have been implemented using a custom-built hardware simulator based on empirical modeling of the real EEG signals. this shown various performance modes of the system and confirms the detection of pre-seizure state for a number of parameters related to the patients such as age, gender, etc… By using this type of fuzzy logic classifier, we were able to get over 90% ac

关键词： Brain computer interface

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the influence of sampling on imbalanced data classification 8

The influence of sampling on imbalanced data classification

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8th Brazilian conference on intelligent Systems, BRACIS 2019

作者： Barella, Victor H. Garcia, Luís P.F. De Carvalho, André C.P.L.F. ICMC/University of São Paulo São Carlos Brazil CIC/University of Brasilia Brasília Brazil

ISBN: (纸本)9781728142531

Classification tasks using imbalanced data are not challenging on their own. When the classes are linearly separable, a regular classification algorithm usually induces predictive models able to distinguish the classes properly. Imbalanced data poses difficulty for the minority class when the training sets have classes overlapping or a complex border decision. Assessing these characteristics is fundamental to understand the classification task difficulty and to choose adequate pre-processing techniques for imbalanced data. Measures able to identify the complexity of a classification task for a given dataset have been proposed. these measures use different criteria to identify how difficult it is to induce a classifier from a dataset. In this paper, we investigate the use of data complexity measures to estimate the best sample size for data imbalance pre-processing techniques. For such, this paper assesses the predictive performance and the data complexity of real datasets after applying pre-processing techniques using different sample sizes. According to experiments, the data complexity measures are a tool to help in choosing a proper sample size to improve the predictive performance of the classifiers. We also observe that only the difficulty of predicting the minority class is not enough when dealing with sampling. As an alternative to deal with this deficiency, we suggest a combination of the data complexity of both classes. © 2019 IEEE.

关键词： Sampling

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Harmful comments extraction from a Bulletin Board System using word meaning and impression on thread context

Harmful comments extraction from a Bulletin Board System usi...

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international Symposium on Soft Computing and intelligent Systems (SCIS)

作者： Yoko Nishihara Kazuki Iwasa Junichi Fukumoto Ryosuke Yamanishi College of Information Science and Engineering Ritsumeikan University Shiga Japan

Harmful documents make readers unpleasant on the Web. In order to hide the harmful documents from the public, machine learning methods have been proposed, which learn words used in harmful documents and hide them automatically. the learned words often have bad meanings. though word meanings are not changed, word impression may be changed on context. Even if a word with bad impression is contained in a document, the previous learning methods can not learn the word, and fail to hide documents. We select the following approach: word impression may be changed on context. If a word has been used with other words of good meaning, it is considered that impression of the word is also good. In contrast, if a word has been used with others of bad meaning, impression of the word may be bad. this paper proposes a new extraction method of harmful comments in a thread of a Bulletin Board System. the proposed method extracts comments using word meanings and word impression on thread context. We evaluated the proposed method using comments collected from four threads in Japanese BBS "2-channel." the averaged precision of extraction was 0.47, and the averaged recall was 0.68. We verified that the proposed method was suitable for extraction of harmful comments from a thread of a BBS.

关键词： Context data mining Educational institutions TV Web pages Postal services

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automated Classification of Medical Images Using Convolutional Neural Networks

Automated Classification of Medical Images Using Convolution...

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international conference on Computing and Networking Technology (ICCNT)

作者： N. Muthuvairavan Pillai S Manimala Aruna Sri Rongali Anchit Gugnani A Senthil Kumar G. V. Sriramakrishnan Computer Science and Business Systems R.M.D Engineering College Kavarapettai Thiruvallur district Computer science and engineering University SJCE JSS Science and technology University Mysore Karnataka India Department Information Technology Emphasis/Information Technology Ph.D University and city with the country University of The Cumberlands US Physiotherapy Amity University Jaipur Rajasthan Artificial Intelligent department Shri Vishnu Engineering College for Women Bhimavaram AP Department Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Tamilnadu

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Medical imagery has been increasingly used in diagnosing and treating various diseases, leading to advances in healthcare. While humans usually lead the qualitative interpretation of visual features, automated Classification of Medical Images Using Convolution Neural Networks (CNNs) has gained traction for accurate image classification. CNNs are a type of deep learning algorithm that attempts to mimic the structure of the human brain and are ideal for image classification due to their convolution layers that can detect patterns within the image. these convolution layers enable the networks to learn the characteristics of the data through a series of convolution and pooling operations. Additionally, they can rapidly identify relationships and patterns within the dataset and extract features from the input image, thus allowing a more accurate classification. With the increasing availability of medical imaging data, advancements in the field of ML/AI has enabled the use of CNNs for automated medical image classification, helping clinicians to diagnose diseases accurately with less time and effort.

关键词： Training Image segmentation Accuracy Convolution Medical services Feature extraction Convolutional neural networks Medical diagnostic imaging X-ray imaging Image classification

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