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Handwritten Character Recognition Using Active Semi-supervised learning 1

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19th international conference on intelligent data engineering and automated learning (IDEAL)

作者： Inkeaw, Papangkorn Bootkrajang, Jakramate Goncalves, Teresa Chaijaruwanich, Jeerayut Chiang Mai Univ Dept Comp Sci Fac Sci Chiang Mai 50200 Thailand Univ Evora Dept Informat P-7000671 Evora Portugal

ISBN: (数字)9783030034931

ISBN: (纸本)9783030034931;9783030034924

Constructing a handwritten character recognition model is considered challenging partly due to the high variety of handwriting styles and the limited amount of training data. In practice, only a handful of labeled examples from limited number of writers are provided during the training of the model. Still, a large collection of already available unlabeled handwritten character data from several sources are often left unused. To alleviate the problem of small training sample size, we propose a graph-based active semi-supervised learning approach for handwritten character recognizer construction. the method iteratively builds a neighborhood graph of all examples including the unlabeled ones, assigns pseudo labels to the unlabeled data and retrains the model. Additionally, the label of the least confident pseudo label according to a newly proposed uncertainty measure is to be requested from the oracle. Experiments on NIST handwritten digits dataset demonstrated that the proposed learning method better utilizes the unlabeled data compared to existing approaches as measured by recognition accuracy. In addition, our active learning strategy is also more effective compared to baseline strategies.

关键词： Handwritten character recognition Semi-supervised learning Active learning

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One size does not fit all: An ensemble approach towards information extraction from adverse drug event narratives 11

One size does not fit all: An ensemble approach towards info...

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11th international conference on Health Informatics, HEALthINF 2018 - Part of 11th international Joint conference on Biomedical engineering Systems and Technologies, BIOSTEC 2018

作者： Wunnava, Susmitha Qin, Xiao Kakar, Tabassum Kong, Xiangnan Rundensteiner, Elke A. Sahoo, Sanjay K. De, Suranjan Computer Science Department Worcester Polytechnic Institute WorcesterMA United States Center for Drug Evaluation and Research U.S. Food and Drug Administration Silver SpringMD United States

ISBN: (纸本)9789897582813

Recognizing named entities in Adverse Drug Reactions narratives is a fundamental step towards extracting valuable patient information from unstructured text into a structured thus actionable format. this then unlocks advanced data analytics towards intelligent pharmacovigilance. Yet existing biomedical named entity recognition (NER) tools are limited in their ability to identify certain entity types from these domain-specific narratives and result in significant performance differences in terms of accuracy. To address these challenges, we propose an ensemble approach that integrates a rich variety of named entity recognizers to procure the final result. First, one critical problem faced by NER in the biomedical context is that the data is highly skewed. that is, only 1% of words belong to a certain medical entity type, such as, the reason for medication usage compared to all other non-reason words. We propose a balanced, under-sampled bagging strategy that is dependent on the imbalance level to overcome the class imbalance problem. Second, we present an ensemble of heterogeneous recognizers approach that leverages a novel ensemble combiner. Our experimental results show that for biomedical text datasets: (i) a balanced learning environment along with an Ensemble of Heterogeneous Classifiers constantly improves the performance over individual base learners and, (ii) stacking-based ensemble combiner methods outperform simple Majority Voting by 0.30 F-measure. Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

关键词： data Analytics

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the effectiveness of data augmentation for detection of gastrointestinal diseases from endoscopical images 5

The effectiveness of data augmentation for detection of gast...

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5th international conference on Bioimaging, BIOIMAGING 2018 - Part of 11th international Joint conference on Biomedical engineering Systems and Technologies, BIOSTEC 2018

作者： Asperti, Andrea Mastronardo, Claudio University of Bologna Mura Anteo Zamboni 7 Bologna40127 Italy

ISBN: (纸本)9789897582783

the lack, due to privacy concerns, of large public databases of medical pathologies is a well-known and major problem, substantially hindering the application of deep learning techniques in this field. In this article, we investigate the possibility to supply to the deficiency in the number of data by means of data augmentation techniques, working on the recent Kvasir dataset (Pogorelov et al., 2017) of endoscopical images of gastrointestinal diseases. the dataset comprises 4,000 colored images labeled and verified by medical endoscopists, covering a few common pathologies at different anatomical landmarks: Z-line, pylorus and cecum. We show how the application of data augmentation techniques allows to achieve sensible improvements of the classification with respect to previous approaches, both in terms of precision and recall. Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

关键词： Pathology

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Cross-Subject Emotion Recognition with a Decision Tree Classifier Based on Sequential Backward Selection

Cross-Subject Emotion Recognition with a Decision Tree Class...

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international conference on intelligent Human-Machine Systems and Cybernetics, IHMSC

作者： Wenge Jiang Guangyuan Liu Xingcong Zhao Fu Yang School of Electronic and Information Engineering Southwest University Chongqing China Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Southwest University Chongqing China

Many of the current emotion recognitions are intra-subject, the reason is that recognize cross-subject emotions have always been difficult. In this paper, we proposed a decision tree classifier based on sequential backward selection (DT-SBS), and "leave-one-subject-out" verification strategy was used for evaluating performance of cross-subject emotion recognition. Power spectral density (PSD) was extracted from EEG signals as feature set. the emotion recognition performance of the method we proposed was examined on EEG data of a group of subjects. the pictures were utilized to stimulate the emotion of 12 subjects and we collected their EEG signals (PEEG), the mean recognition accuracy of 65.8%. the dataset for emotion analysis using physiological signals (DEAP) was utilized to verify the method we proposed in this paper and the mean recognition accuracy of 65.2%. Compared with the performance reported in the existing literature, the method we proposed in this paper was effective in cross-subject emotion recognition. In addition, the emotion recognition performance of different rhythms were explored, we found that the mean recognition accuracy of PSD in the gamma rhythm achieved the best mean recognition accuracy compared with the other rhythms; the high frequency bands contained higher emotional information than the low frequency bands.

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Understanding Learner's Drop-Out in MOOCs 19th

Understanding Learner's Drop-Out in MOOCs

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19th international conference on intelligent data engineering and automated learning (IDEAL)

作者： Itani, Alya Brisson, Laurent Garlatti, Serge UBL IMT Atlantique Lab STICC F-29238 Brest France

ISBN: (纸本)9783030034931;9783030034924

this paper focuses on anticipating the drop-out among MOOC learners and helping in the identification of the reasons behind this drop-out. the main reasons are those related to course design and learners behavior, according to the requirements of the MOOC provider OpenClassrooms. Two critical business needs are identified in this context. First, the accurate detection of at-risk droppers, which allows sending automated motivational feedback to prevent learners drop-out. Second, the investigation of possible drop-out reasons, which allows making the necessary personalized interventions. To meet these needs, we present a supervised machine learning based drop-out prediction system that uses Predictive algorithms (Random Forest and Gradient Boosting) for automated intervention solutions, and Explicative algorithms (Logistic Regression, and Decision Tree) for personalized intervention solutions. the performed experimentations cover three main axes;(1) Implementing an enhanced reliable dropout-prediction system that detects at-risk droppers at different specified instants throughout the course. (2) Introducing and testing the effect of advanced features related to the trajectories of learners' engagement with the course (backward jumps, frequent jumps, inactivity time evolution). (3) Offering a preliminary insight on how to use readable classifiers to help determine possible reasons for drop-out. the findings of the mentioned experimental axes prove the viability of reaching the expected intervention strategies.

关键词： learning analytics Supervised machine learning Massive Open Online Courses Modeling drop-out

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Communication Skills Personal Trainer Based on Viola-Jones Object Detection Algorithm 19th

Communication Skills Personal Trainer Based on Viola-Jones O...

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19th international conference on intelligent data engineering and automated learning (IDEAL)

作者： Pardo Pertierra, Alvaro Gil Gonzalez, Ana B. Teira Lafuente, Javier de Luis Reboredo, Ana Univ Salamanca Dept Comp & Automat Fac Sci Plaza Merced S-N Salamanca 37008 Spain Univ Salamanca Philosophy Fac Salamanca Spain

ISBN: (纸本)9783030034931;9783030034924

the paper presents a personal training system that allows the user to use non-verbal communication techniques that help him improve his way of speaking in public. the tool is a software solution that implements algorithms to identify elements of non-verbal communication (NVC), such as the positions of the head, hands and trunk of the users making use of cascading classifiers. It presents the developed technology and the different characteristics of the application that is an important part of the treatment in artificial vision techniques in the detection of NVC and its training.

关键词： Oratorical coach Artificial vision Viola-Jones algorithm AdaBoost Non-verbal communication (NVC) learning environment

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A New One-Class Classification Method with Multiple Encoder-Decoder Pairs for Images

A New One-Class Classification Method with Multiple Encoder-...

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international conference on intelligent Human-Machine Systems and Cybernetics, IHMSC

作者： Dongxiang Chen Chungang Yan Mimi Wang Department of Electronics and Information Engineering Tongji University The Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China

One of the main destinations of image classification methods is to screen out the images belonging to the target class (positive) and identify the images of other classes (negative). Although most classifiers are trained on both positive samples and negative samples, in reality, negative samples are often unavailable. One-class classifiers trained only on positive samples, are proposed to solve this problem. However, how to train effectively a classifier remains a daunting challenge. Considering the success of deep learning in the field of computer vision in recent years, we propose a one-class classification model for images based on convolutional neural network (CNN). the model consists of two parts of networks with different responsibilities. the network of the first part works as the discriminator, used to identify whether the images are positive. the networks of the second part, each of which consists of an encoder-decoder pair work as the guiders of the discriminator. they guide the discriminator to learn what images it should identify to be negative. Different encoder-decoder pairs can restore images to different degrees. Images restored to different degrees can be used to train the discriminator. the discriminator learns that images under a specific restored degree don't belong to the target class. the experiment results on CIFAR-10 show that our method can achieve good performance, with less difficulty in training than the GAN-based counterparts'.

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Evaluation of closed-loop feedback system delay a time-critical perspective for neurofeedback training 11

Evaluation of closed-loop feedback system delay a time-criti...

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11th international conference on Biomedical Electronics and Devices, BIODEVICES 2018 - Part of 11th international Joint conference on Biomedical engineering Systems and Technologies, BIOSTEC 2018

作者： Tidare, Jonatan Åstrand, Elaine Ekström, Martin School of Innovation Design and Engineering Mälardalen University Västerås Sweden

ISBN: (纸本)9789897582776

Neurofeedback in real-time has proven effective when subjects learn to control a BCI. To facilitate learning, a closed-loop feedback system should provide neurofeedback with maximal accuracy and minimal delay. In this article, we propose a modular system for real-time neurofeedback experiments and evaluate its performance as a function of increased stress level applied to the system. the system shows stable behavior and decent performance when streaming with many EEG channels (36-72) and 500-5000 Hz, which is common in BCI setups. With very low data loads (1 channel, 500-1000 Hz) the performance dropped significantly and the system became highly unpredictable. We show that the system delays did not correlate linearly with the stress-level applied to the system, emphasizing the importance of system delay tests before conducting real-time BCI-experiments. Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

关键词： Robotics

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Forecasting thailand’s Precipitation with Cascading Model of CNN and GRU

Forecasting Thailand’s Precipitation with Cascading Model o...

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international conference on Information Technology and Electrical engineering (ICITEE)

作者： Fuenglada Manokij Kanoksri Sarinnapakorn Peerapon Vateekul Department of Computer Engineering Faculty of Engineering Chulalongkorn University Big Data Analytics and IoT Center (CUBIC) Chulalongkorn University Bangkok Thailand Hydro-Informatics Institute (Public Organization) Ministry of Higher Education Science Research and Innovation Bangkok Thailand

Precipitation prediction is necessary to use in water management, especially in thailand, it can be applied for various ways, such as flood warning, agriculture planning, etc. there are many prior attempts to forecast rainfall from the rain-gauge station. Some deployed traditional machine learning approaches: ARIMA, k-NN, etc. Recently, deep learning approach has shown promising results in this task. However, the accuracy is still limited since the raining period throughout the year in thailand is very scarce, so most rainfall amount is zero. In this paper, we propose to cascade two deep learning networks to tackle this problem: one is a classification model to classify whether it rains or not, and the other is a regression model to predict rainfall amount. Our network is a combination of CNN and GRU, where CNN aims to capture relationship between various sensors and GRU aims to capture time-series information. Furthermore, we also perform multi-step forecasting by applying a rolling mechanism that uses the predicted rainfall and involved features to predict the next 6 steps. the experiment was conducted on hourly rainfall dataset for 6 years (2013-2018) provided from the public government sector in thailand. We use RMSE as performance metric to evaluate three periods of rainfall: Overall, Rain, and non-rain periods and the results show that our cascading model is the winner with only 4.53% in term of RMSE which is the average percentage of difference from over all regions.

关键词： Deep learning Rain Predictive models data models Sensors Forecasting Task analysis

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Compound Local Binary Pattern and Enhanced Jaya Optimized Extreme learning Machine for Digital Mammogram Classification 1

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19th international conference on intelligent data engineering and automated learning (IDEAL)

作者： Mohanty, Figlu Rup, Suvendu Dash, Bodhisattva Int Inst Informat Technol Image & Video Proc Lab Bhubaneswar Odisha India

ISBN: (数字)9783030034931

ISBN: (纸本)9783030034931;9783030034924

the fatality rate due to breast cancer still continues to remain high across the world and women are the frequent sufferers of this cancer. Mammography is one of the powerful imaging modalities to detect and diagnose cancer at its early stage effectively. A computer-aided diagnosis (CAD) system is a potential tool which analyses the mammographic images to reach a correct decision. the present work aims at developing a CAD framework which can classify the mammograms accurately. this work has primarily four stages. First, contrast limited adaptive histogram equalization (CLAHE) is used for pre-processing. Second, feature extraction is realized using compound local binary pattern (CLBP) followed by principal component analysis (PCA) for feature reduction. Finally, an enhanced Jaya-based extreme learning machine is utilized to classify the mammograms as normal or abnormal, and further, benign or malignant. the success rate in terms of classification accuracy achieves 100% and 99.48% for MIAS and DDSM datasets, respectively.

关键词： Mammography Computer-aided diagnosis Compound local binary pattern Jaya algorithm Extreme learning machine

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