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

共 7177 条记录，以下是4231-4240 订阅

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Reward Function Using Inverse Reinforcement learning and Fuzzy Reasoning

Reward Function Using Inverse Reinforcement Learning and Fuz...

引用

international Symposium on Soft Computing and intelligent Systems (SCIS)

作者： Yuta Kato Masayoshi Kanoh Tsuyoshi Nakamura Graduate School of Engineering Chukyo University Nagoya Japan School of Engineering Chukyo University Nagoya Japan Graduate School of Engineering Nagoya Institute of Technology Nagoya Japan

ISBN: (数字)9781728197326

ISBN: (纸本)9781728197333

A reward function estimated with inverse reinforcement learning has been used to determine a method for controlling a robot. Inverse reinforcement learning requires observed sequences of actions to estimate a reward function. Few models of the sequences give the optimal motion of the robot; therefore, a suboptimal one may be given. However, the suboptimal sequences include some errors and ambiguities. In this paper, we propose a method for quantifying the ambiguity of the reward function, which is designed with inverse reinforcement learning using fuzzy reasoning.

关键词： Reinforcement learning Safety Robots Fuzzy reasoning Mathematical model Trajectory Security

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Predicting Customers Intending To Cancel Credit Card Subscriptions Using Machine learning Algorithms: A Case Study 11

Predicting Customers Intending To Cancel Credit Card Subscri...

引用

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

作者： Altinisik, Fehim Yilmaz, Hasan Huseyin Turkiye Vakiflar Bankasi TAO EBIS Directorate R&D & Digital Transformat Istanbul Turkey

ISBN: (纸本)9786050112757

Following paper introduces analysis of machine learning algorithms implemented in order to predict customers of commercial bank who may be in risk of cancelling credit card subscriptions by following three months after a year or less activity. An analysis of various data preprocessing, sampling and structuring procedures using a feature set made up of 106 variables -describing customers' transaction activity, demographics, overall contentment and relative information to consumer experience- also shared. Study also includes performance comparison of Deep Neural Networks against other generic machine learning algorithms on two different cases. Deep Neural Networks were the point of interest of this study and it turns out, them to perform better than generic machine learning algorithms.

关键词： Deep neural networks

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A Deep learning-Based Surface Defect Inspection System for Smartphone Glass 20th

A Deep Learning-Based Surface Defect Inspection System for S...

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

作者： Go, Gwang-Myong Bu, Seok-Jun Cho, Sung-Bae Yonsei Univ Dept Comp Sci Seoul South Korea

ISBN: (纸本)9783030336073;9783030336066

In recent years, convolutional neural network has become a solution to many image processing problems due to high performance. It is particularly useful for applications in automated optical inspection systems related to industrial applications. this paper proposes a system that combines the defect information, which is meta data, with the defect image by modeling. Our model for classification consists of a separate model for embedding location information in order to utilize the defective locations classified as defective candidates and ensemble with the model for classification to enhance the overall system performance. the proposed system incorporates class activation map for preprocessing and augmentation for image acquisition and classification through optical system, and feedback of classification performance by constructing a system for defect detection. Experiment with real-world dataset shows that the proposed system achieved 97.4% accuracy and through various other experiments, we verified that our system is applicable.

关键词： Deep learning Convolutional neural network Class activation map Smartphone glass inspection Defect detection Augmentation Image preprocessing

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CSEDU 2019 - Proceedings of the 11th international conference on Computer Supported Education

CSEDU 2019 - Proceedings of the 11th International Conferenc...

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11th international conference on Computer Supported Education, CSEDU 2019

ISBN: (纸本)9789897583674

the proceedings contain 142 papers. the topics discussed include: From ‘minimal’ to ‘maximal’ digital experiences for learning: focusing on the learner-centered design and use of technology;computer supported CABLE: collaborative argumentation-based learning;available soon;efficient computing of the bellman equation in a POMDP-based intelligent tutoring system;ontology-based analysis of game designs for software refactoring;generating education in-game data: the case of an ancient theatre serious game;towards the ranking of web-pages for educational purposes;performance evaluation of universities and colleges based on method of principal component analysis and data envelopment analysis;teaching computer programming to post-millennial kids: overview of goals, activities and supporting tools;and effects of proactive personality and social centrality on learning performance in SPOCs.

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DF-VLAD: Deepfake Video Detection based on Feature Aggregation

DF-VLAD: Deepfake Video Detection based on Feature Aggregati...

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IEEE international Symposium on Information (IT) in Medicine and Education, ITME

作者： Yun Huang Zhiming Luo Miaohui Zhang Wei Liu Shaozi Li Xiamen University Xiamen China Institute of Energy Research Jiangxi Academy of Sciences Nanchang China School of Software East China Jiaotong University Nanchang China Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Wuyi University Nanping China

ISBN: (纸本)9781728194202

With the rapid development of Deepfake technology, face video forgery can produce highly deceptive video content and bring serious security threats. the detection of this kind of fake video is more urgent and challenging. Most of the existing detection methods regard this problem as a common binary classification problem, and using a simple average or maximum as the prediction of video results can easily lead to missed detection or false detection. While the video-based detection work such as LSTM, in Deepfake detection, too much focus on timing modeling will affect the performance of Deepfake video detection to a certain extent. Based on this, this paper proposes a VLAD-based aggregation module DF-VLAD, which advances the aggregation of multiple frames from the output layer to the feature layer, which on the one hand makes the aggregation more flexible, on the other hand, it also uses the objective function of forgery detection to directly guide the learning of frame-level depth representation; On the other hand, this paper deals with this problem as a special fine-grained classification problem, because the difference between fake face and real face is very subtle. It is found that the existing face forgery methods such as Face2Face and NeuralTextures leave some common artifacts in the spatial domain. Different forgery methods produce different artifacts, while natural faces have more similar features. To make the model pay more attention to artifacts, a forgery trace capture model based on the fusion of self-attention mechanism and channel attention mechanism is proposed in this paper. Like other fine-grained classification methods, note intentions are used to guide the network to pay attention to key parts of the face. Experimental results on different public data sets show that the proposed method achieves the latest performance.

关键词： Education Feature extraction Linear programming Forgery Timing Security Face detection

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An Efficient Machine learning Method to Solve Imbalanced data in Metabolic Disease Prediction 11

An Efficient Machine Learning Method to Solve Imbalanced Dat...

引用

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

作者： Cecchini, Vania Nguyen, thanh-Phuong Pfau, thomas De landtsheer, Sebastien Sauter, thomas Univ Luxembourg Life Sci Res Unit Esch Sur Alzette Luxembourg Megeno Esch Sur Alzette Luxembourg

ISBN: (纸本)9781728130033

the increase of obesity, its related diseases and the high incidence of metabolic diseases as a whole, constitute a major public health problem on a global scale. New strategies that allow for the discovery of novel metabolic disease-related genes are necessary to develop new treatments. In this paper, we proposed an efficient method to predict metabolic disease genes, solving the problem of imbalanced data. the method combined protein-protein interactions and miRNA-target interactions to construct integrated networks, whose topological properties can be used as features to train machine learning classifiers. We applied different strategies to optimize imbalanced class. the best model of gradient boosting achieved a significant F1-score of 0.82. When testing the model with non-disease genes, we predicted 549 candidates, out of which 123 were validated indirectly from literature to be related to metabolic diseases. the remaining genes' functions were investigated by gene enrichment analysis, revealing their association with diseases known to co-occur with metabolic diseases, such as cancer and cardiovascular conditions. these results indicated that this method contributed to the identification of novel metabolic disease-related genes.

关键词： metabolic disease protein-protein interaction network miRNA-target interaction machine learning disease gene prediction imbalanced data

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Inducement of Multivariate factors in Cardiac Disease Prediction with Machine learning Techniques substantiated with Analytics 2019

Inducement of Multivariate factors in Cardiac Disease Predic...

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11th international conference on Computer and Automation engineering (ICCAE)

作者： Rajalakshmi, Selvaraj Madhav, Kuthadi Venu Abhishek, Ranjan BIUST Fac Sci Dept CS & IS Palapye Botswana Botho Univ Fac Comp Maseru Lesotho

ISBN: (纸本)9781450362870

Cardiac disorder prediction is a certain requirement for preserving the lives of millions of people suffering from cardiac problems in all ages. Machine learning is a new dimension of prediction in the field of data mining as it is incorporated with mathematical techniques and procedures to provide right insight into the accurate prediction of disease with the best outcomes. the major objective of the research is to predict the cardiac disease using multivariate factors which involve;change in heart beat during exercise, oxygen supply to heart, angina responses and heart disease history. the major features attributed to the prediction of the heart disease occurrence is identified in three levels as normal, mild and severe respectively. the indication of the heart disease levels is incorporated by the rulesets formed by the multivariate factors to form a prediction network. the prediction of the multivariate component is induced with sequential application of logistic regression and linear discriminant analysis algorithms which are based on machine learning techniques. the implementation is controlled with MATLAB design and algorithm is applied on the software to predict the levels of heart disease and report in Excel format. the Analytic is performed using sensitivity and specificity measures and the accuracy is achieved with 98.2% to achieve reliability.

关键词： multivariate factors heart disease machine learning techniques logistic regression linear discriminant analysis exercise angina

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Global Q-learning Approach for Power Allocation in Femtocell Networks 20th

Global Q-Learning Approach for Power Allocation in Femtocell...

引用

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

作者： Alenezi, Abdulmajeed M. Hamdi, Khairi Univ Manchester Sch Elect & Elect Engn Manchester Lancs England Islamic Univ Medinah Madinah Saudi Arabia

ISBN: (纸本)9783030336073;9783030336066

In dense femtocell network, the complexity of the resource allocation increases significantly as the network becomes denser, which limits the performance of the network. the usage of reinforcement learning to solve the resource allocation problem showed promising results compared to conventional methods. In this work, we use global Q-learning approach on the macro base station to solve the resource allocation problem in a dense and complex network. We propose a new reward function that can be implemented on a centralized Q-learning and achieve good results in terms of maintaining the quality of service for the macro user and maximizing the sum capacity of the femtocell users. In comparison to other reward functions, the proposed reward function maintained both the QoS for the macro user and fairness among all femtocell users.

关键词： Femtocell Q-learning Resource allocation

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Deep Convolutional Neural Networks Based on Image data Augmentation for Visual Object Recognition 20th

Deep Convolutional Neural Networks Based on Image Data Augme...

引用

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

作者： Jayech, Khaoula Univ Sousse Ecole Natl Ingn Sousse LATIS Lab Adv Technol & Intelligent Syst Sousse 4023 Tunisia

ISBN: (纸本)9783030336073;9783030336066

Deep Neural Networks (DNNs) have achieved a great success in machine learning. Among a lot of DNN structures, Deep Convolutional Neural Networks (DCNNs) are currently the main tool in the state-of-the-art variety of classification tasks like visual object recognition and handwriting and speech recognition. Despite wide perspectives, DCNNs have still some challenges to deal with. In previous work, we demonstrated the effectiveness of using some regularization techniques such as the dropout to enhance the performance of DCNNs. However, DCNNs need enough training data or even a class balance within datasets to conduct better results. To resolve this problem, some researchers have evoked different data augmentation approaches. this paper presents an extension of a later study. In this work, we conducted and compared the results of many experiments on CIFAR-10, STL-10 and SVHN using variant techniques of data augmentation combined with regularization techniques. the analysis results show that with the right use of data augmentation approaches, it is possible to achieve good results and outperform the state-of-the-art in this field.

关键词： Deep learning DCNN Image data augmentation Object recognition

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CSEDU 2019 - Proceedings of the 11th international conference on Computer Supported Education

CSEDU 2019 - Proceedings of the 11th International Conferenc...

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11th international conference on Computer Supported Education, CSEDU 2019

ISBN: (纸本)9789897583674

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