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检索条件"任意字段=Proceedings of the 2019 2nd International Conference on Signal Processing and Machine Learning"

共 1333 条记录，以下是51-60 订阅

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Local, Mid-Level and Convolutional Features Fusion Using Multiple Kernel learning for Image Classification 2

Local, Mid-Level and Convolutional Features Fusion Using Mul...

引用

2nd IEEE international conference on Information Communication and signal processing (ICICSP)

作者： Lu, Yao Zhang, Hui Xie, Bojun Hebei Univ Key Lab Machine Learning & Computat Intelligence Coll Math & Informat Sci Baoding Peoples R China

ISBN: (纸本)9781728151021

Feature representation and feature fusion are important factors in image classification problem. In this paper, the local features, mid-level features and convolutional features are combined using the multiple kernel learning method. Experimental results show that the local features, mid-level features and convolutional features can be fused effectively to improve the classification performance about 4%-6% on several popular benchmarks.

关键词： convolutional feature mid-level feature multiple kernel learning image classification

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Applying an Ensemble learning Method for Improving Multi-label Classification Performance 2

Applying an Ensemble Learning Method for Improving Multi-lab...

引用

2nd international conference of signal processing and Intelligent Systems (ICSPIS)

作者： Mahdavi-Shahri, Amirreza Houshmand, Mahboobeh Yaghoobi, Mahdi Jalali, Mehrdad Islamic Azad Univ Dept Comp Engn Mashhad Branch Mashhad Iran

ISBN: (纸本)9781509058204

in recent years, multi-label classification problem has become a controversial issue. In this kind of classification, each sample is associated with a set of class labels. Ensemble approaches are supervised learning algorithms in which an operator takes a number of learning algorithms, namely base-level algorithms and combines their outcomes to make an estimation. The simplest form of ensemble learning is to train the base-level algorithms on random subsets of data and then let them vote for the most popular classifications or average the predictions of the base-level algorithms. In this study, an ensemble learning method is proposed for improving multi-label classification evaluation criteria. We have compared our method with well-known base-level algorithms on some data sets. Experiment results show the proposed approach outperforms the base well-known classifiers for the multi-label classification problem.

关键词： machine learning Multi-label Classification Singlelabel classification Ensemble learning

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Screening of Moderate Traumatic Brain Injury from Power Feature of Resting State Electroencephalography using Support Vector machine 2019

Screening of Moderate Traumatic Brain Injury from Power Feat...

引用

2nd international conference on Electronics and Electrical Engineering Technology (EEET) / 3rd international conference on Biomedical Engineering and Bioinformatics (ICBEB)

作者： Lai, Chi Qin Abdullah, Mohd Zaid Abdullah, Jafri Malin Azman, Azlinda Ibrahim, Haidi Univ Sains Malaysia Sch Elect & Elect Engn Engn Campus Nibong Tebal 14300 Pulau Pinang Malaysia Univ Sains Malaysia Dept Neurosci Sch Med Sci Kubang Kerian 16150 Kelantan Malaysia Univ Sains Malaysia Sch Social Sci George Town 11800 Pulau Pinang Malaysia

ISBN: (纸本)9781450372145

Traumatic brain injury (TBI) needs to be identified faster, so that suitable treatment can be planned properly. Normally, the severity of TBI is evaluated through the study from computed tomography (CT) or magnetic resonance imaging (MRI). Unfortunately, the number of CT scanners and MRI scanners is limited. Therefore, it is impractical to directly do CT or MRI scan to all patients without screening. Thus, this research investigates a method for screening moderate TBI patient. Data from resting state 63-channels electroencephalography is used in this work. Power of the signal is extracted from alpha, beta, theta and gamma frequency bands. This work utilizes a support vector machine, which is one of machine learning approaches, to identify moderate TBI patients. From the experimental results, it is shown that the average power from alpha or theta band gives the best accuracy score, which is at 70.83%.

关键词： Biomedical signal processing Feature extraction machine learning Moderate traumatic brain injury Power feature Resting state electroencephalography

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Model of Sentiment Analysis with Deep learning in Social Network Environment 2

Model of Sentiment Analysis with Deep Learning in Social Net...

引用

IEEE 2nd international conference on Electronic Information and Communication Technology (ICEICT)

作者： Wanda, Putra Huang JinJie Univ Respati Yogyakarta Fac Sci & Technol Yogyakarta 55281 Indonesia Harbin Univ Sci & Technol Sch Automat Engn Harbin Peoples R China

ISBN: (纸本)9781538692981

Currently, the digital environment such as social network needs real-time and adaptive security model. Deep learning is becoming increasingly popular for various applications. In this research, we proposed a Dynamic Deep learning algorithm, dubbed Dynamic Convolutional Neural Networks (CNN). Different from common CNN, it assigns similar signal parts to the same CNN channel and solves signal alignment. Therefore, it can better deal with the problem of data noise, alignment, and other data variations. We achieve an increase in CNN graph's performance with dynamic k-max pooling model with a benchmark dataset for sentiment analysis.

关键词： OSN Sentiment Dynamic CNN Social Messenger

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Knowledge-graph based Proactive Dialogue Generation with Improved Meta-learning 2020

Knowledge-graph based Proactive Dialogue Generation with Imp...

引用

2nd international conference on Image processing and machine Vision / international conference on Pattern Recognition and machine learning (IPMV)

作者： Xu, Hongcai Bao, Junpeng Wang, Junqing Xi An Jiao Tong Univ Xian 710049 Shaanxi Peoples R China

ISBN: (纸本)9781450388412

Knowledge graph-based dialogue systems can narrow down knowledge candidates for generating informative and diverse responses with the use of prior information, e.g., triple attributes or graph paths. However, most current knowledge graphs (KG) cover incomplete domain-specific knowledge. To overcome this drawback, we propose a knowledge graph based proactive dialogue generation model (KgDg) with three components, improved model-agnostic meta-learning algorithm (MAML), knowledge selection in knowledge triplets embedding, and knowledge aware proactive response generator. For knowledge triplets embedding and selection, we formulate it as a problem of sentence embedding to better capture semantic information. Our improved MAML algorithm is capable of learning general features from a limited number of knowledge graphs, which can also quickly adapt to dialogue generation with unseen knowledge triplets. Extensive experiments are conducted on a knowledge aware dialogue dataset (DuConv). The results show that KgDg adapts both fast and well to knowledge graph based dialogue generation and outperforms state-of-the-art baselines.

关键词： Knowledge graph Proactive dialogue generation Meta-learning

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Chinese Texts Classification System 2

Chinese Texts Classification System

引用

2nd IEEE international conference on Information and Computer Technologies (ICICT)

作者： Zhu, Meng Yang, Xudong Beijing Univ Posts & Telecommun Beijing Peoples R China

ISBN: (纸本)9781728133232

In this article, we designed an automatic Chinese text classification system aiming to implement a system for classifying news texts. We propose two improved classification algorithms as two different choices for users to choose and then our system uses the chosen method for the obtaining of the classified result of the input text. There are two improved algorithms, one is k-Bayes using hierarchy conception based on NB method in machine learning field and another one adds attention layer to the convolutional neural network in deep learning field. Through experiments, our results showed that improved classification algorithms had better accuracy than based algorithms and our system is useful for making classifying news texts more reasonably and effectively.

关键词： natural language processing machine learning deep learning Naive bayes CNN automatic system

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Pattern Recognition by Pattern Inversion 2

Pattern Recognition by Pattern Inversion

引用

2nd international conference on Image and Video processing, and Artificial Intelligence (IPVAI)

作者： Mikhailov, Alexei Karavay, Mikhail Russian Acad Sci ICS Moscow Russia

ISBN: (数字)9781510634107

ISBN: (纸本)9781510634107

Alternative approach to pattern recognition is discussed that amounts to operations on inverse patterns and resembles working of Google-type search engines. Unlike neural networks that iteratively calculate weights within many a learning cycles, inverse patterns-based paradigm (neural cortex) does not use weights and follows a challenging learning trend that attempts to achieve a human-like generalization from a single example.

关键词： machine learning neural networks pattern recognition inverse patterns neural cortex search engines

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Glottal signal Analysis for Voice Pathology 2

Glottal Signal Analysis for Voice Pathology

引用

2nd international conference on Innovations in Electronics, signal processing and Communication, IESC 2019

作者： Mittal, Vikas Sharma, R.K. NIT School of VLSI Design and Embedded System Kurukshetra India NIT Department of Electronics and Communication Engineering Kurukshetra India

ISBN: (纸本)9781728107448

The work present in this paper is focusing on a method for the detection of voice disorders related to the pathologies of vocal folds. It extracts glottal signal parameters by using inverse filtering method. Support Vector machine (SVM) and k-nearest neighboring (k-NN) classifiers are used for differentiating healthy and non-healthy persons. The voice samples of the person belonging to both the categories were acquired using a specialized voice recorder. SVM provided 98.5 % whereas k-NN provided 88.2 % accuracy. Voice signals of patients affected with vocal cord nodules were also collected and tested with same classifier. The Accuracy of 78.9 % and 70.4% were obtained with SVM and k-NN respectively. © 2019 IEEE.

关键词： Support vector machines

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Research on Blockchain Anomalous Behavior Detection Technology 2

Research on Blockchain Anomalous Behavior Detection Technolo...

引用

2nd international conference on signal processing and Intelligent Computing, SPIC 2024

作者： Sheng, Xia Li, Aihua Sun, Fengxu Shenyang Ligong University School of Information Science and Engineering Shenyang China

ISBN: (纸本)9798350368888

For blockchain transactions, how to distinguish normal transactions from abnormal transactions is a very critical issue. To this end, we introduce deep learning as a capable tool for abnormal behavior detection detection, and its excellent learning ability shines in this field. In this paper, we propose a deep learning time series Transformer model based on deep learning combined with ResNet (Residual Network) for anomaly detection and classification of Bitcoin dataset. The Elliptic dataset is used and the dataset is top-filled by manually crawling the latest Bitcoin dataset, and the Random Forest algorithm is used for feature selection, and finally 16 features are selected for classification. After testing under different sampling methods the Transformer model combined with residual network shows good detection results compared to structures such as single machine learning algorithm SVM (Support Vector machine) and other deep learning models CNN (Convolutional Neural Network). © 2024 IEEE.

关键词： Adversarial machine learning

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machine learning on Biomedical Images: Interactive learning, Transfer learning, Class Imbalance, and Beyond 2

Machine Learning on Biomedical Images: Interactive Learning,...

引用

2nd IEEE international conference on Multimedia Information processing and Retrieval (MIPR)

作者： Khan, Naimul Mefraz Abraham, Nabila Hon, Marcia Guan, Ling Ryerson Univ Ryerson Multimedia Res Lab Toronto ON Canada

ISBN: (纸本)9781728111988

In this paper, we highlight three issues that limit performance of machine learning on biomedical images, and tackle them through 3 case studies: 1) Interactive machine learning (IML): we show how IML can drastically improve exploration time and quality of direct volume rendering. 2) transfer learning: we show how transfer learning along with intelligent pre-processing can result in better Alzheimer's diagnosis using a much smaller training set 3) data imbalance: we show how our novel focal Tversky loss function can provide better segmentation results taking into account the imbalanced nature of segmentation datasets. The case studies are accompanied by in-depth analytical discussion of results with possible future directions.

关键词： Training Image color analysis Biomedical imaging Computer architecture Feature extraction Rendering (computer graphics) machine learning

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