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共 588 条记录，以下是241-250 订阅

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Pruning Extreme Wavelets learning machine by Automatic Relevance Determination 20th

Pruning Extreme Wavelets Learning Machine by Automatic Relev...

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20th international Conference on Engineering Applications of Neural Networks (EANN) /1st international workshop on Pervasive Intelligence (PEINT)

作者： de Campos Souza, Paulo V. Silva Araujo, Vinicius J. Araujo, Vanessa S. Batista, Lucas O. Guimaraes, Augusto J. CEFET MG Ave Amazonas5253Nova Suica Belo Horizonte MG Brazil Fac UNA Betim Ave Gov Valadares640 Ctr Betim MG Brazil

ISBN: (纸本)9783030202576;9783030202569

Extreme learning machines are used for various contexts in artificial intelligence, such as for classifying patterns, performing time series prediction and regression problems, and being a more viable solution for training hidden layer weights to determine values of the learning model. However, the essence, the model determines that these weights should be determined randomly, and the Moore Penrose pseudoinverse will define only the weights that will act in the output layer. Random weights make this learning a black box because there is no relationship between the hidden layer weights and the problem data. This paper proposes the initialization of weights and bias in the hidden layer through the Wavelets transform that allows the two parameters, previously initialized at random, to be more representative about the problem domain, allowing the frequency range of the input patterns of the network to aid in the definition of weights of the ELM hidden layer. To assist in the representativeness of the data, a technique of selection of characteristics based on automatic relevance determination will be applied to the selection of the most characteristic dimensions of the problem. To compose the network structure, activation functions of the type rectified linear unit, also called ReLU, were used. The proposed model was submitted to the classification test of binary patterns in real classes, and the results show that the proposition of this work assists in bringing better accuracy to the classification results, and thus can be considered a feasible proposition to the training of neural networks that use extreme learning machine.

关键词： Extreme learning machine Wavelets ReLU Automatic relevance determination pattern classification

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students' Performance Prediction Model Using Meta-classifier Approach 20th

Students' Performance Prediction Model Using Meta-classifier...

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20th international Conference on Engineering Applications of Neural Networks (EANN) /1st international workshop on Pervasive Intelligence (PEINT)

作者： Hassan, Hasniza Anuar, Syahid Ahmad, Nor Bahiah Univ Teknol Malaysia Johor Baharu 81310 Johor Malaysia Univ Teknol Malaysia Kuala Lumpur 54100 Malaysia

ISBN: (纸本)9783030202576;9783030202569

students' performance is vitally important at all stages of education, particularly for Higher Education Institutions. One of the most important issues is to improve the performance and quality of students enrolled. The initial symptom of at-risks' students need to be observed and earlier preventive measures are required to be carried out so as to determine the cause of students' dropout rate. Hence, the purpose of this research is to identify factors influencing students' performance using educational data mining techniques. In order to achieve this, data from different sources is employed into a single platform for pre-processing and modelling. The design of the study is divided into 6 different phases (data collection, data integration, data pre-processing such as cleaning, normalization, and transformation, feature selection, patterns extraction and model optimization as well as evaluation. The datasets were collected from a students' information system and e-learning system from a public university in Malaysia, while sample data from the Faculty of Engineering were used accordingly. This study also employed the use of academic, demographical, economical and behaviour e-learning features, in which 8 different group models were developed using 3 base-classifiers;Decision Tree, Artificial Neural Network and Support Vector machine, and 5 multi-classifiers;Random Forest, Bagging, AdaBoost, stacking and Majority Vote classifier. Finally, the highest accuracy of the classifier model was optimized. At the end, new students' Performance Prediction Model was developed. The result proves that combination demographics with behaviour using a meta-classifier model with optimized hyper parameter produced better accuracy to predict students' performance.

关键词： student performance prediction Educational data mining E-learning Higher education Classification Ensemble model

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A study of English Neologisms Through Large-Scale Probabilistic Indexing of Bentham’s Manuscripts 2nd

A Study of English Neologisms Through Large-Scale Probabilis...

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2nd international workshop on Recent Advances in Digital Security: Biometrics and Forensics, BioFor 2019, 1st international workshop on pattern recognition for Cultural Heritage, PatReCH 2019, 1st international workshop eHealth in the Big data and Deep learning Era, e-BADLE 2019, international workshop on Deep Understanding Shopper Behaviors and Interactions in Intelligent Retail Environments, DEEPRETAIL 2019 and Industrial session held at the 20th international Conference on Image Analysis and Processing, ICIAP 2019

作者： Toselli, Alejandro H. Romero, Verónica Vidal, Enrique Sánchez, Joan Andreu Seaward, Louise Schofield, Philip PRHLT Universitat Politècnica de València Camí de Vera s/n València46022 Spain Bentham Project UCL Laws 4-8 Endsleigh Gardens LondonWC1H 0EG United Kingdom

ISBN: (纸本)9783030307530

Probabilistic indexes (PI) are obtained from untranscribed handwritten text images by means of recently introduced lexicon-free, query-by-string, probabilistic keyword spotting techniques. PIs have proven to be a powerful tool that allow efficient, free textual searching in very large collections of handwritten historical documents. PIs convey uncertain information about the textual contents of the document images. However, text uncertainty is accurately modeled by the associated lexical probability distributions, which can be conveniently exploited in many applications. As an example of these applications, here we study the dating of a number of English neologisms in the large collection of Bentham’s manuscripts, which encompass images. The statistical techniques used for neologism dating are theoretically motivated and experiments on this collection are reported. Among other interesting contributions of this study, it provides sound evidence that some commonly assumed neologism introduction dates need to be revised. © 2019, Springer Nature Switzerland AG.

关键词： Probability distributions

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Multi-agent monitoring system for heat loss mapping of multi-story buildings 1

Multi-agent monitoring system for heat loss mapping of multi...

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1st international workshop on Information-Communication Technologies and Embedded Systems, ICT and ES 2019

作者： Burlachenko, Ivan Zhuravska, Iryna Tohoiev, Oleksii Ukhan, Yehor Tiutiunyk, Yevhen Petro Mohyla Black Sea National University Mykolaiv Ukraine LeadsMarket LLC Woodland HillsCA United States

In this paper, the problem of developing a multi-agent method for detecting the places of heat energy leaks on the multi-story buildings using machine learning is solved. Efficient data processing of scanning areas for the heat energy leak monitoring was achieved using the multi-agent monitoring system (MAMS) that can perform calculations in the cloud conditionally. Features of the monitoring system with the integration of an analytical model for presenting a heat loss map with an account of multiple autonomous separated UAV's for temperature measurements were contained. The MAMS reliability of the synchronization model between simultaneous localization and mapping method and generated heat loss map based on temperature measurements was confirmed. It has been experimentally proven that theoretical assumptions and accuracy for experimental usage during the multi-story building leaks analysis are sufficient. The recognition time of markers of the front of the building is in the range from 0 to 27 s. In this case, with the proposed model ?NN, the CPU load during the execution of tasks did not exceed 26%. Copyright © 2019 for this paper by its authors.

关键词： machine learning

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Deep learning and Change Detection for Fall recognition 20th

Deep Learning and Change Detection for Fall Recognition

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20th international Conference on Engineering Applications of Neural Networks (EANN) /1st international workshop on Pervasive Intelligence (PEINT)

作者： Tasoulis, Sotiris K. Mallis, Georgios I. Georgakopoulos, Spiros V. Vrahatis, Aristidis G. Plagianakos, Vassilis P. Maglogiannis, Ilias G. Univ Thessaly Dept Comp Sci & Biomed Informat Volos Greece Univ Piraeus Dept Digital Syst Piraeus Greece

ISBN: (纸本)9783030202576;9783030202569

Early fall detection is a crucial research challenge since the time delay from fall to first aid is a key factor that determines the consequences of a fall. Wearable sensors allow a reliable way for daily-life activities tracking, able to detect immediately a high-risk fall via a machine learning framework. Towards this direction, accelerometer devices are used widely for the assessment of fall risk. Although there is a plethora of studies under this perspective with promising results, several challenges still remain such as the extremely demanding data and power management as well as the discovery of false positive falls. In this work we propose a complete methodology based on the combination of the computationally demanding convolutional neural networks along with a lightweight change detection method. Our basic assumption is that it is possible to control computational resources for the operation of a classifier, suffice to be activated when a strong change in user's movements is identified. The proposed methodology was applied to real experimental data providing reliable results that justify the original hypothesis.

关键词： Deep learning Change detection Fall detection Wearable devices

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1D CNN Based Human Respiration pattern recognition using Ultra Wideband Radar 1

1D CNN Based Human Respiration Pattern Recognition using Ult...

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1st international Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Kim, Seong-Hoon Han, Gi-Tae Gachon Univ Dept IT Convergence Engn Seongnam Si South Korea Gachon Univ Dept Comp Engn Seongnam Si South Korea

ISBN: (纸本)9781538678220

The respiration status of a person is one of the vital signs that can be used to check the health condition of the person. The respiration status has been measured in various ways in the medical and healthcare sectors. Contact type sensors were conventionally used to measure respiration. The contact type sensors have been used primarily in the medical sector, because they can be only used in a limited environment. Recent studies have evaluated the ways of detecting human respiration patterns using Ultra-Wideband (UWB) Radar, which relies on noncontact type sensors. Previous studies evaluated the apnea pattern during sleep by analyzing the respiration signals acquired by UWB Radar using a principal component analysis (PCA). However, it is necessary to measure various respiration patterns in addition to apnea in order to accurately analyze the health condition of an individual in the healthcare sector. Therefore, this study proposed a method to recognize four respiration patterns based on the 1D convolutional neural network from the respiration signals acquired from UWB Radar. The proposed method extracts the eupnea, bradypnea, tachypnea, and apnea respiration patterns from UWB Radar and composes a learning dataset. The proposed method learned data through 1D CNN and the recognition accuracy was measured. The results of this study revealed that the accuracy of the proposed method was up to 15% higher than that of the conventional classification algorithms (i.e., PCA and Support Vector machine (SVM)).

关键词： UWB Radar Vital Signs Respiration pattern Healthcare 1D Convolutional Neural Network

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A 12-Lead ECG Arrhythmia Classification Method Based on 1D Densely Connected CNN 1st

A 12-Lead ECG Arrhythmia Classification Method Based on 1D D...

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1st international workshop on machine learning and Medical Engineering for Cardiovasvular Healthcare, MLMECH 2019, and the 8th international Joint workshops on Computing and Visualization for Intravascular Imaging and Computer Assisted stenting, CVII-stENT 2019, held in conjunction with 22nd international Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019

作者： Wang, Chunli Yang, Shan Tang, Xun Li, Bin Chengdu Spaceon Electronics Co. Ltd. Chengdu China

ISBN: (纸本)9783030333263

In this work, we have proposed an electrocardiogram (ECG) arrhythmia classification method for short 12-lead ECG records to identify nine types (one normal type and eight abnormal types), using a 1D densely connected CNN which is a relatively novel convolutional neural network (CNN) model and shows outstanding performance in the field of pattern recognition. Firstly, noticing that ECG records are one dimensional time series with different noise levels, several wavelet-based shrinkage filtering methods were adopted to the ECG records for data augmentation. Secondly, each ECG record was divided into segments with a fixed length of 10 s, and the total number of segments for an ECG record is 10. And then, 10 segments were fed into an optimized 1D densely connected CNN for training. And lastly, a threshold vector was trained for the multi-label classification since each record may have more than one abnormal types. The approach has been validated against The First China ECG Intelligent Competition data set, obtaining a final F1 score of 0.873 and 0.863 on the validation set and test set, respectively. © 2019, Springer Nature Switzerland AG.

关键词： Electrocardiograms

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Attribute reduction for partially labeled data based on hypergraph models 31

Attribute reduction for partially labeled data based on hype...

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31st IEEE international Conference on Tools with Artificial Intelligence (ICTAI)

作者： Xie, Xiaojun Qin, Xiaolin Huang, Guangmei Zhao, Wei Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Peoples R China McMaster Univ Dept Comp & Software Hamilton ON L8S 4K1 Canada Guangxi Normal Univ Fac Educ Guilin 541001 Peoples R China

ISBN: (纸本)9781728137988

Attribute reduction is an important process in many fields, such as knowledge discovery, data mining, machine learning, pattern recognition and so on. However, totally labeled data are quite hard to obtain in real life. Thus, we often have to face the situation that not all the data have been associated with labels in advance. Due to the coexistence of labeled and unlabeled data, attribute reduction problem for partially labeled data becomes more complex and challenging. Many scholars have devoted themselves to solve this problem in the past few years. But current algorithms for partially labeled data are not efficient enough in terms of time complexity. To address this issue, we propose a hypergraph model, where two types of induced hypergraphs are designed from partially labeled decision systems. Then, a fast algorithm based on low-complexity heuristics is raised to compute the minimum vertex cover of a hypergraph. Finally, we propose two types of hypergraph models-based attribute reduction algorithms for partially labeled decision systems. Experimental results on broadly used data sets show that the feasibility and efficiency of our proposed algorithms.

关键词： attribute reduction hypergraph partially labeled decision system minimum vertex cover

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Using machine learning Techniques to Earlier Predict student's Performance 1

Using Machine Learning Techniques to Earlier Predict Student...

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1st international Conference of the Indonesian-Association-for-pattern-recognition (INAPR)

作者： Tanuar, Evawaty Heryadi, Yaya Lukas Abbas, Bahtiar Saleh Gaol, Ford Lumban Bina Nusantara Univ Sch Comp Sci Comp Sci Dept Jakarta 11480 Indonesia Bina Nusantara Univ BINUS Grad Program Comp Sci Jakarta 11480 Indonesia Bina Nusantara Univ Dept Comp Sci BINUS Grad Program Comp Sci Jakarta 11480 Indonesia Univ Katolik Indonesia Atma Jaya Fac Engn CERG Jakarta 12930 Indonesia

ISBN: (纸本)9781538694220

Education field is rich of data, and machine learning used in this field are increasing lately. Based on their first semester result, using machine learning techniques, the student's final year result (GPA) can be predicted. The data used in this experiment are from the computer science subjects, 6 subjects, 1 laboratories results and the GPA on their graduation year. The techniques used in this experiment are Generalized Linear Model, Deep learning and Decision Tree. From this result, what are the important factors that impact on the result can be extracted to help the students prepared themselves earlier.

关键词： machine learning data mining student's Performance - GPA Soft Computing

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A Multi-label learning Method to Detect Arrhythmia Based on 12-Lead ECGs 1st

A Multi-label Learning Method to Detect Arrhythmia Based on ...

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作者： Zhu, Jinjing Xin, Kaifa Zhao, Qingqing Zhang, Yue Division of Information Science and Technology Tsinghua University Beijing China Renmin Hospital of Wuhan University Wuhan University Wuhan China

ISBN: (纸本)9783030333263

Cardiovascular disease (CVD) is one of the most serious diseases that harm human life and gives a huge burden to the health care system. Recent advances in deep learning have achieved great success in object detection, speech and image recognition. Although deep learning has been applied to the detection of arrhythmia, detection accuracy is limited because of three major issues: 1. Each ECG signal maybe contains more than one-label information;2. It is hard to classify ECG with different lengths;3. data imbalance problem is severe for arrhythmia detection. In this paper, we present a multi-label learning algorithm to address the class imbalance and detection on ECGs with different durations. We utilize Deep Convolutional Generative Adversarial Networks (DCGANs) and Wasserstein GAN-Gradient Penalty (WGAN-GP) to generate new positive samples and use two losses to balance the importance between positive samples and negative samples. Moreover, we construct a Squeeze and Excitation-ResNet (SE-ResNet) module for normal rhythm and arrhythmia detection. In order to solve the multi-label classification problem, we train nine different binary classifiers for each category and determine which types of rhythm the ECG signals belong to. Experimental results on The ECG Intelligence Challenge 2019 dataset demonstrate that our multi-label learning method achieves competitive performance in multi-label ECGs classification. © 2019, Springer Nature Switzerland AG.

关键词： Electrocardiograms

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