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检索条件"任意字段=11th International Conference on Intelligent Data Engineering and Automated Learning"

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A Method Based on Filter Bank Common Spatial Pattern for Multiclass Motor Imagery BCI 20th

A Method Based on Filter Bank Common Spatial Pattern for Mul...

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20th international conference on intelligent data engineering and automated learning (IDEAL)

作者： Xia, Ziqing Xia, Likun Ma, Ming Capital Normal Univ Coll Informat Engn Beijing Peoples R China Lab Neural Comp & Intelligent Percept NCIP New York NY 10027 USA Int Sci & Technol Cooperat Base Elect Syst Reliab Beijing Peoples R China Beijing Adv Innovat Ctr Imaging Technol Beijing Peoples R China Stanford Univ Sch Med Stanford CA 94305 USA

ISBN: (纸本)9783030336172;9783030336165

the Common Spatial Pattern (CSP) algorithm is capable of solving the binary classification problem for the motor image task brain-computer interface (BCI). this paper proposes a novel method based on the Filter Bank Common Spatial Pattern (FBCSP) termed the Multiscale and Overlapping FBCSP (MO-FBCSP). We extend the CSP algorithm for multiclass by using the one-versus-one (OvO) strategy. Multiple periods are selected and combined with the overlapping spectrum of the filter bank which contains useful information. this method is evaluated on the benchmark BCI Competition IV dataset 2a with 9 subjects. An average accuracy of 80% was achieved with the random forest (RF) classifier, and the corresponding kappa value was 0.734. Quantitative results have shown that the proposed scheme outperforms the classical FBCSP algorithm by over 12%.

关键词： Brain-computer interface Motor imagery Machine learning EEG

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Effective Text data Preprocessing Technique for Sentiment Analysis in Social Media data 11

Effective Text Data Preprocessing Technique for Sentiment An...

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11th international conference on Knowledge and Systems engineering (KSE)

作者： Pradha, Saurav Halgamuge, Malka N. Nguyen Tran Quoc Vinh Charles Sturt Univ Sch Comp & Math Melbourne Vic Australia Univ Melbourne Dept Elect & Elect Engn Melbourne Vic 3010 Australia Univ Da Nang Fac Informat Technol Univ Sci & Educ Da Nang Vietnam

ISBN: (纸本)9781728130033

In the big data era, data is made in real-time or closer to real-time. thus, businesses can utilize this ever-growing volume of data for the data-driven or information-driven decision-making process to improve their businesses. Social media, like Twitter, generates an enormous amount of such data. However, social media data are often unstructured and difficult to manage. Hence, this study proposes an effective text data preprocessing technique and develop an algorithm to train the Support Vector Machine (SVM), Deep learning (DL) and Naive Bayes (NB) classifiers to process Twitter data. We develop an algorithm that weights the sentiment score in terms of weight of hashtag and cleaned text. In this study, we (i) compare different preprocessing techniques on the data collected from Twitter using various techniques such as (stemming, lemmatization and spelling correction) to obtain the efficient method (ii) develop an algorithm to weight the scores of the hashtag and cleaned text to obtain the sentiment. We retrieved N=1,314,000 Twitter data, and we compared the popularity of two products, Google Now and Amazon Alexa. Using our data preprocessing algorithm and sentiment weight score algorithm, we train SVM, DL, NB models. the results show that stemming technique performed best in terms of computational speed. Additionally, the accuracy of the algorithm was tested against manually sorted sentiments and sentiments produced before text data preprocessing. the result demonstrated that the impact produced by the algorithm was close to the manually annotated sentiments. In terms of model performance, the SVM performed better with the accuracy of 90.3%, perhaps, due to the unstructured nature of Twitter data. Previous studies used conventional techniques;hence, no precise methods were utilized on cleaning the text. therefore, our approach confirms that proper text data preprocessing technique plays a significant role in the prediction accuracy and computational time of the cla

关键词： Social media data Twitter big data text data preprocessing sentiment analysis Deep learning Support Vector Machine Naive Bayes Google Now Amazon Alexa

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A Deep learning-based Method for Turkish Text Detection from Videos 11

A Deep Learning-based Method for Turkish Text Detection from...

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11th international conference on Electrical and Electronics engineering (ELECO)

作者： Rasheed, Jawad Jamil, Akhtar Dogru, Hasibe Busra Tilki, Sahra Yesiltepe, Mirsat Istanbul Sabahattin Zaim Univ Dept Comp Engn Istanbul Turkey Yildiz Tech Univ Dept Math Engn Istanbul Turkey

ISBN: (纸本)9786050112757

the text appearing in videos provides useful information, which can be exploited for developing automatic video indexing and retrieval systems. In this study, we integrated a heuristic and a deep learning-based method using Convolutional Neural Network (CNN) for automatic text extraction from videos. the two independent steps used for text extraction are;candidate text region detection and classification. In first step, rectangular regions were detected that potentially contain text by applying heuristics, which includes morphological processing and geometrical constraints. then, the obtained candidate text regions were passed through several layers of CNN, that first produced convolutional feature map and then classified the candidate regions into either text or not-text classes. A dataset was prepared by collecting videos from various Turkish channels. 70% of the data was used to train the network while 30% for validation. Experiments showed that our proposed method achieved state-of-the-art performance on our dataset.

关键词： Convolution

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Multi-step Power Consumption Forecasting in thailand Using Dual-Stage Attentional LSTM 11

Multi-step Power Consumption Forecasting in Thailand Using D...

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11th international conference on Information Technology and Electrical engineering (ICITEE)

作者： Siridhipakul, Chukwan Vateekul, Peerapon Chulalongkorn Univ Big Data Analyt & IoT Ctr CUBIC Dept Comp Engn Fac Engn Bangkok Thailand

ISBN: (纸本)9781728140551

Our task is to forecast the next day's power consumption in the half-hour interval for a total of 48 intervals. there are many studies that proposed models for power consumption forecasting problems but most of the previously proposed techniques focus on impact from different time step to power consumption, the importance of different features was not considered in these works. the Dual-Stage Attention-Based Recurrent Neural Network (DA-RNN) is the state-of-the-art in time series forecasting that considers both varying impacts from different time features for one-step-ahead forecasting. On the contrary, our work focuses on multi-step-ahead forecasting. In this paper, we aim to apply Dual-Stage Attentional LSTM for a multi-step-ahead power consumption forecasting problem. Experiments were conducted on thailand's power consumption data consisting of 5 control areas, weather data, and day type (weekday/weekend). We use RMSE and MAPE as evaluation metrics, the results showed that the Dual-Stage Attentional LSTM outperformed traditional models in both metrics.

关键词： Power consumption forecasting Deep learning Attention Mechanism Long short-term memory

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Representation learning of Knowledge Graphs with Multi-scale Capsule Network 20th

Representation Learning of Knowledge Graphs with Multi-scale...

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20th international conference on intelligent data engineering and automated learning (IDEAL)

作者： Cheng, Jingwei Yang, Zhi Dang, Jinming Pan, Chunguang Zhang, Fu Northeastern Univ Coll Comp Sci & Engn Shenyang Peoples R China

ISBN: (纸本)9783030336073;9783030336066

Representation learning of knowledge graphs has gained wide attention in the field of natural language processing. Most existing knowledge representation models for knowledge graphs embed triples into a continuous low-dimensional vector space through a simple linear transformation. In spite of high computation efficiency, the fitting ability of these models is suboptimal. In this paper, we propose a multi-scale capsule network to model relations between embedding vectors from a deep perspective. We use convolution kernels with different sizes of windows in the convolutional layer inside a Capsule network to extract semantic features of entities and relations in triples. these semantic features are then represented as a continuous vector through a routing process algorithm in the capsule layer. the modulus of this vector is used as the score of confidence of correctness of a triple. Experiments show that the proposed model obtains better performance than state-of-the-art embedding models for the task of knowledge graph completion over two benchmarks, WN18RR and FB15k-237.

关键词： Representation learning Capsule network Multi-scale Dynamic routing Knowledge graph completion

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Optimization of the Numeric and Categorical Attribute Weights in KAMILA Mixed data Clustering Algorithm 1

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20th international conference on intelligent data engineering and automated learning (IDEAL)

作者： Martarelli, Nadia Junqueira Nagano, Marcelo Seido Univ Sao Paulo Lab Appl Operat Res BR-13566590 Sao Paulo Brazil

ISBN: (数字)9783030336073

ISBN: (纸本)9783030336073;9783030336066

the mixed data clustering algorithms have been timidly emerging since the end of the last century. One of the last algorithms proposed for this data-type has been KAMILA (KAy-means for MIxed LArge data) algorithm. While the KAMILA has outperformed the previous mixed data algorithms results, it has some gaps. Among them is the definition of numerical and categorical variable weights, which is a user-defined parameter or, by default, equal to one for all features. Hence, we propose an optimization algorithm called Biased Random-Key Genetic Algorithm for Features Weighting (BRKGAFW) to accomplish the weighting of the numerical and categorical variables in the KAMILA algorithm. the experiment relied on six real-world mixed data sets and two baseline algorithms to perform the comparison, which are the KAMILA with default weight definition, and the KAMILA with weight definition done by the traditional genetic algorithm. the results have revealed the proposed algorithm overperformed the baseline algorithms results in all data sets.

关键词： Attributes weighting Mixed data clustering Biased Random-Key Genetic Algorithm KAMILA algorithm

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Prediction of Autism-Related Genes Using a New Clustering-Based Under-Sampling Method 11

Prediction of Autism-Related Genes Using a New Clustering-Ba...

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11th international conference on Knowledge and Systems engineering (KSE)

作者： Xuan tho Dang Duong Hung Bui thi Hong Nguyen Tran Quoc Vinh Nguyen Dang Hung Tran Hanoi Natl Univ Educ Fac Informat Technol Hanoi Vietnam Hanoi Trade Union Univ Fac Informat Technol Hanoi Vietnam Univ Da Nang Univ Sci & Educ Fac Informat Technol Danang Vietnam

ISBN: (纸本)9781728130033

Autism is one of the neurological disorders that occurs in children. there are many causes of autism, one of which is genetic factors. therefore, in order to find effective treatments, we need to discover the genes which relate to autism disease. In this paper, we use a computational approach to train a model that can predict new autism-related candidate genes. the methodology combines different data sources such as protein-protein interaction networks, microRNAs (miRNA)target network and known autism-related genes into an integrated network. the structural properties of this network are represented as a vector dataset and a binary classification problem is formulated. However, because the number of known autism-related genes is very small, we face an imbalance data classification problem. To solve this issue, an under-sampling clustering-based data balancing algorithm has been proposed. Training classifiers with machine learning models such as SVMs, k-NN, and RFs, we obtained results of 1-3% higher in G-mean measures when comparing to cases without using any data balancing strategies. these results implied that our proposed model may contribute to finding new autism-related gene candidates.

关键词： imbalance data clustering-based under-sampling classification autism-related gene protein-protein interaction network miRNA-mRNA network

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Multi-agent Reinforcement learning for Joint Wireless and Computational Resource Allocation in Mobile Edge Computing System 11th

Multi-agent Reinforcement Learning for Joint Wireless and Co...

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11th EAI Annual international conference on Ad Hoc Networks (ADHOCNETS)

作者： Zhang, Yawen Xia, Weiwei Yan, Feng Cheng, Huaqing Shen, Lianfeng Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China

ISBN: (纸本)9783030372620;9783030372613

Mobile edge computing (MEC) is a new paradigm to provide computing capabilities at the edge of pervasive radio access networks in close proximity to intelligent terminals. In this paper, a resource allocation strategy based on the variable learning rate multi-agent reinforcement learning (VLR-MARL) algorithm is proposed in the MEC system to maximize the long term utility of all intelligent terminals while ensuring the intelligent terminals' quality of service requirement. the novelty of this algorithm is that each agent only needs to maintain its own action value function so that the computationally expensive issue with the large action space can be avoided. Moreover, the learning rate is changed according to the expected payoff of the current strategy to speed up convergence and get the optimal solution. Simulation results show our algorithm performs better than other reinforcement learning algorithm both on the learning speed and users' long term utilities.

关键词： Mobile edge computing Joint resource allocation Multi-agent reinforcement learning Variable learning rate

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Opening user model data for motivation and learning: the case of an adaptive multiplication game 11

Opening user model data for motivation and learning: The cas...

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11th international conference on Computer Supported Education, CSEDU 2019

作者： Leonardou, Angeliki Rigou, Maria Garofalakis, John Dept. of Computer Engineering and Informatics University of Patras Rio Campus26500 Greece

ISBN: (纸本)9789897583674

Multiplication table fluency is of core importance, as it consists a fundamental stage of mathematics education. It is a common phenomenon that pupils face difficulties in perceiving this knowledge and achieving multiplication skills. this paper presents an adaptive multiplication game for assessing and gradually improving multiplication skills. the game also incorporates Open Learner Model elements which expose parts of the learner model to the user through easily perceivable visualizations for improving self-reflection, fostering self-regulated learning and increasing user motivation. the game has been tested with a representative sample of primary school students and based on the data collected the game and the Open Learner Model’s features have been received positively. © 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

关键词： Motivation

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Study of intelligent Question Answering System Based on Ontology

Study of Intelligent Question Answering System Based on Onto...

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IEEE international conference on Software engineering and Service Sciences (ICSESS)

作者： Jin Xu Wei Zhang The Army Infantry Academy of PLA Nanchang Jiangxi Province China Military Representative Office Nanchang Jiangxi Province China

ISBN: (数字)9781728165790

ISBN: (纸本)9781728165806

this paper designs intelligent Question Answering System (IQAS) by a combined use of the concept of ontology and search engine. the system can help users to obtain more accurate answers. It raises the issue of user word segmentation and keyword extraction, and searches keyword-related information on the web, and then returns results and saves them to the database, which enables users to extract easily. It is found through the search that the results are more accurate than those before, and the users have more choices.

关键词： Systematics Semantics Ontologies Search engines Knowledge discovery data mining Software engineering

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