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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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An improved deep learning based classification of human white blood cell images

An improved deep learning based classification of human whit...

引用

international conference on Electrical and Computer engineering (ICECE)

作者： Md. Abu Ismail Siddique Abu Zahid Bin Aziz Abdul Matin Electrical & Computer Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh

ISBN: (数字)9781665422543

ISBN: (纸本)9781665446617

White blood cells (WBC) are part of the immune systems which defend both infectious diseases and foreign invaders. there are various types of white blood cells in our body and each of these blood cells has a specific function in our body. the differential test is the traditional way to classify white blood cells in that it calculates the percentage of different types of white blood cells. In this test, the efficiency is low and time-consuming. Various machine learning and deep learning methods have been developed over the years that produced good results. In this work, we applied a deep learning based convolutional neural network (CNN) called “SqueezeNet” to classify white blood cells. After fine-tuning the hyperparameters, we trained our model and tested its performance in the testing dataset. Our method achieved 93.8% accuracy in the test data which is better than the existing classifiers. this proves that our method can be a useful approach for this task.

关键词： White blood cells Deep learning Neural networks Cells (biology) Tools Task analysis Testing

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Comparative Analysis of Spectral and Cepstral Feature Extraction Techniques for Phoneme Modelling 11th

Comparative Analysis of Spectral and Cepstral Feature Extrac...

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11th international conference on Multimedia and Network Information Systems (MISSI)

作者： Korvel, Grazina Kurasova, Olga Kostek, Bozena Vilnius Univ Inst Data Sci & Digital Technol Akad Str 4 LT-04812 Vilnius Lithuania Gdansk Univ Technol Audio Acoust Lab Fac Elect Telecommun & Informat G Narutowicza 11-12 PL-80233 Gdansk Poland

ISBN: (纸本)9783319986784;9783319986777

Phoneme parameter extraction framework based on spectral and cepstral parameters is proposed. Using this framework, the phoneme signal is divided into frames and Hamming window is used. the performances are evaluated for recognition of Lithuanian vowel and semivowel phonemes. Different feature sets without noise as well as at different level of noise are considered. Two classical machine learning methods (Naive Bayes and Support Vector Machine) are used for classifying each problem, separately. the experiment results show that cepstral parameters give higher accuracies than spectral parameters. Moreover, cepstral parameters give better performance compared to spectral parameters in noisy conditions.

关键词： Parameter extraction Machine learning Spectrum Cepstrum

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A Novel Mice Interaction Recognition Method By Using Machine learning 11

A Novel Mice Interaction Recognition Method By Using Machine...

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11th international conference on Digital Image Processing (ICDIP)

作者： Pan, Xu Zhu, Yuesheng Luo, Guibo Guo, Biao Peking Univ Shenzhen Grad Sch Commun & Informat Secur Lab Shenzhen 518055 Peoples R China

ISBN: (数字)9781510630765

ISBN: (纸本)9781510630765

Mice interaction recognition has been widely employed in animal observation experiments since it can provide useful biological signals for researchers to identify the social behaviors of animal models. Rather than manual observation, automatic observation systems based on computer vision can be used to detect and track the mice behaviors dynamically, which has become popular in mice interaction recognition. In order to enhance the accuracy of recognition, a novel mice interaction recognition method by using machine learning is proposed in this paper. A new elliptical model is developed to fit every mouse and to extract its motion and shape features. For selecting the optimal feature, we investigate the influence of different features on the result. We improve the average recognition result on the novel dataset RatSI.

关键词： Mice interaction recognition computer vision machine learning method hidden markov model

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An ensemble deep active learning method for intent classification 3

An ensemble deep active learning method for intent classific...

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3rd international conference on Computer Science and Artificial Intelligence, CSAI 2019 and its Workshop - the 11th international conference on Information and Multimedia Technology, ICIMT 2019

作者： Zhang, Leihan Zhang, Le Wangxuan Institute of Computer Technology Peking University No. 128 Zhongguancun North Street Haidian District Beijing100871 China Yonyou Network Co Ltd No. 68 Beiqing Road Haidian District Beijing100094 China Beijing Key Laboratory of Internet Culture and Digital Dissemination Research Beijing Information Science and Technology University No. 35 North Fourth Ring Road Middle Chaoyang District Beijing100101 China

ISBN: (纸本)9781450376273

Intent classification plays a primary and critical role in intelligent dialogue systems. However, faced with the lack of labeled data, the training of robust intent classification model is timeconsuming and costly. thanks to the powerful pre-trained model and active learning, it's possible to construct an integrated method to fulfill this task efficiently. therefore, we propose an ensemble deep active learning method, which constructs intent classifier based on BERT and uses an ensemble sampling method to choose informative data for efficient training. Experimental results on both Chinese and English intent classification datasets suggest that the proposed ensemble deep active learning method can achieve state-of-the-art performance with less than half of the training data. In addition, the performance of the proposed method is stable and scalable for both datasets. In general, the proposed method shows substantial advantages in building intent classifier across different datasets. © 2019 Association for Computing Machinery.

关键词： Classification (of information)

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11th KES international conference on intelligent Decision Technologies, KES-IDT 2019

11th KES International Conference on Intelligent Decision Te...

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11th KES international conference on intelligent Decision Technologies, KES-IDT 2019

ISBN: (纸本)9789811383021

the proceedings contain 31 papers. the special focus in this conference is on intelligent Decision Technologies. the topics include: the study of generators of orthogonal pseudo-random sequences;portraits of orthogonal matrices as a base for discrete textile ornament patterns;training sample generation software;Business AI alignment modeling based on enterprise architecture;eye tracking as a method of neuromarketing for attention research—an empirical analysis using the online appointment booking platform from mercedes-benz;a concept of an interactive web-based machine learning tool for individual machine and production monitoring;benefits of agile project management in an environment of increasing complexity—A transaction cost analysis;potential benefits of digital business models and its processes in the financial and insurance industry;risk-sensitive decision-making under risk constraints with coherent risk measures;an efficient non-blind steering vector estimation technique for robust adaptive beamforming with multistage error feedback;mutual evaluation and solution method;visualization of criteria priorities using a ternary diagram;changes in organizations due to management mechanization (case studies of life insurance companies in Japan);naturality for ranking from pairwise comparisons;upper level processes and projects model building;SPCM with harker method for MDAHP including hierarchical criteria;building a telemedicine system for monitoring the health status and supporting the social adaptation of children with autism spectrum disorders;implementation of digital technologies in pharmaceutical products lifecycle;home hospital e-health centers for barrier-free and cross-border telemedicine;data-driven human movement assessment;the P2P–grid–agent distributed platform: A distributed and dynamic platform for developing and executing large-scale application based on deep learning techniques.

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11th Asia-Pacific Symposium on Internetware, Internetware 2019

11th Asia-Pacific Symposium on Internetware, Internetware 20...

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11th Asia-Pacific Symposium on Internetware, Internetware 2019

ISBN: (纸本)9781450377010

the proceedings contain 20 papers. the topics discussed include: understanding quality of experiences on different mobile browsers;extracting mapping relations for mobile user interface transformation;TransDroid: automatic client-based service evolving in android apps;a preliminary study on data augmentation of deep learning for image classification;a neural-network based code summarization approach by using source code and its call dependencies;an approach to helping developers learn open source projects based on machine learning;speedup automatic program repair using dynamic software updating: an empirical study;and migrating deprecated API to documented replacement: patterns and tool.

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Deep learning Method for Estimating Perplexion in Learners by Educational Support Robots

Deep Learning Method for Estimating Perplexion in Learners b...

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international Symposium on Soft Computing and intelligent Systems (SCIS)

作者： Ryo Yoshizawa Felix Jimenez Kazuhito Murakami Graduate School of Information Science and Technology Aichi Prefectural University Aichi Japan School of Information Science and Technology Aichi Prefectural University Aichi Japan

ISBN: (数字)9781728197326

ISBN: (纸本)9781728197333

Educational support robots have been studied in recent years. this paper focuses on teacher-type robots that behave like teachers. Prior research has demonstrated that teacher-type robots that provide educational support based on the learning achievements of the learners also provide a high learning effect in problem-solving situations. However, conventional teacher-type robots provide learners with learning support when a problem is presented or when the learner requests learning support by pressing a button. It is therefore not possible for conventional teacher-type robots to estimate when a learner is troubled (perplexed) or to autonomously provide learning support when the learner cannot solve a problem. A teacher-type robot that autonomously provides support can allow more effective learning, thereby improving the learning effect. In this study, we developed a teacher-type robot that estimates when a learner is perplexed and autonomously provides learning support. In this paper, we propose the use of a neural network to estimate when a learner is perplexed based on the learner's facial expressions when solving a problem. the proposed method uses a neural network to estimate the level of perplexion based on the probability distribution of seven basic emotions estimated from facial images of learners when applying a CNN model.

关键词： Robots Neural networks data models Probability distribution Estimation Training Pressing

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Transfer learning on Natural YES/NO Questions

Transfer Learning on Natural YES/NO Questions

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

作者： Heju Yin Feng Zhou Xiaoyong Li Junqiang Zheng Kai Liu Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications Beijing China School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China Baidu Company Beijing China

ISBN: (数字)9781728165790

ISBN: (纸本)9781728165806

Although neural network approaches achieve remarkable success on QA, many of them struggle to answer questions that require retrieving relevant factual information from the given paragraph. Inferring from a given paragraph and answering the question to be true or false is an essential part of natural language understanding. To improve the model's inferring ability on Natural YES/NO Question, we provide a simple and effective method. First, we find the tasks related to main task Natural YES/NO Question to fine-tune the model by multi-task learning, then we fine-tune the model on the main task. Results on dataset BoolQ show this method is competitive with other recently published methods, which means transferring from the related datasets through multi-task learning in first stage can save more beneficial information about main task Natural YES/NO. Further analysis show that this method can not only have benefit in this task, it also can be used to other tasks.

关键词： Training conferences Neural networks Natural languages Task analysis Software engineering

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Quantum based Support Vector Machine Identical to Classical Model

Quantum based Support Vector Machine Identical to Classical ...

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international conference on Computing and Networking Technology (ICCNT)

作者： Disha Uke Kapil Kumar Soni Akhtar Rasool Computer Science Engineering Maulana Azad National Institute of Technology Bhopal India

ISBN: (数字)9781728168517

ISBN: (纸本)9781728168524

the paper presents an influence of machine learning techniques that can be efficiently solved using quantum computer. Machine learning is considered as a core concept of modeling and mining large dimensionality data and visualizes data instances as a vector. Support vector is one of the trending methods that use supervised learning for classifying the data efficiently irrespective of linearity or non-linearity in data. As the quantum computations already proved to be inherently parallel and can become one of the key factors in order to reduce the complexities while dealing with the classical machine learning model. the quantum machine learning takes advantage of parallelism and can explore the number of vectors and their dimensions in logarithmic time, and hence it can achieve the computational speedup. Now, the main contribution of the paper is to discuss quantum fundamental, quantum algorithm and then to review over classical equivalent quantum support vector machine along with their training algorithm and the comparative analysis between algorithmic growths. At last, the paper justifies the quantum support vector as a highly efficient model that gains the computational speedup and also shows the possibility of further improvement.

关键词： Support vector machines Kernel Training Quantum computing Optimization Mathematical model Supervised learning

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Transfer learning in GMDH-Type Neural Networks 11th

Transfer Learning in GMDH-Type Neural Networks

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11th international conference on Multimedia and Network Information Systems (MISSI)

作者： Abdullahi, Aminu Akter, Mukti Fed Univ Dutse Dept Comp Sci Dutse Nigeria Univ Bedfordshire Sch Comp Sci Luton Beds England

ISBN: (纸本)9783319986784;9783319986777

Recognition of difficult patterns with the accuracy comparable to that of the human brain is a challenging problem. the ability of the human to excel at this task has motivated the use of Artificial Neural Networks (ANNs) which under certain conditions provide efficient solutions. ANNs are still unable to use the full potential of modular and holistic operations of biological neurons and their networks. the ability of neurons to transfer learned behaviour has inspired an idea to train ANN for a new task by using the behaviour patterns learnt from a related task. the useful patterns transferred from one task to another can significantly reduce the time needed to learn new patterns, and gives the neurons the ability to generalise instead of memorising patterns. In this paper we explore the ability of transfer learning for a face recognition problem by using Group Method of data Handling (GMDH) type of Deep Neural Networks. In our experiments we show that the transfer learning of a GMDH-type neural network has reduced the training time by 31% on a face recognition benchmark.

关键词： Transfer learning Deep Neural Networks GMDH Face recognition

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