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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71-80 订阅

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Assertion Detection in Clinical Natural Language processing: A Knowledge-Poor machine learning Approach 2

Assertion Detection in Clinical Natural Language Processing:...

引用

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

作者： Chen, Long Med Data Quest Inc La Jolla CA 92037 USA

ISBN: (纸本)9781728133232

Natural language processing (NLP) have been recently used to extract clinical information from free text in Electronic Health Record (EHR). In clinical NLP one challenge is that the meaning of clinical entities is heavily affected by assertion modifiers such as negation, uncertain, hypothetical, experiencer and so on. Incorrect assertion assignment could cause inaccurate diagnosis of patients' condition or negatively influence following study like disease modeling. Thus, clinical NLP systems which can detect assertion status of given target medical findings (e.g. disease, symptom) in clinical context are highly demanded. Here in this work, we propose a deep-learning system based on word embedding, RNN and attention mechanism (more specifically: Attention-based Bidirectional Long Short-Term Memory networks) for assertion detection in clinical notes. Unlike previous state-of-art methods which require knowledge input or feature engineering, our system is a knowledge poor machine learning system and can be easily extended or transferred to other domains. The evaluation of our system on public benchmarking corpora demonstrates that a knowledge poor deep-learning system can also achieve high performance for detecting negation and assertions comparing to state-of-the-art systems.

关键词： natural language processing clinical assertion deep learning LSTM attention mechanism

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learning ALGORITHMS FOR ADAPTIVE signal-processing And CONTROL

LEARNING ALGORITHMS FOR ADAPTIVE SIGNAL-PROCESSING AND CONTR...

引用

1993 IEEE international CONF ON NEURAL NETWORKS / 2nd international CONF ON FUZZY SYSTEMS

作者： WIDROW, B LEHR, M BEAUFAYS, F WAN, E BILELLO, M Stanford Univ Stanford United States

Linear and nonlinear adaptive filtering algorithms are described, along with applications to signal processing and control problems such as prediction, modeling, inverse modeling, equalization, echo cancelling, noise ... 详细信息

ISBN: (纸本)0780309995

关键词： Adaptive filtering

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Overview of the 2nd international Workshop on Exploiting Artificial Intelligence Techniques for Data Management (aiDM'19) 19

Overview of the 2nd International Workshop on Exploiting Art...

引用

ACM SIGMOD international conference on Management of Data (SIGMOD)

作者： Bordawekar, Rajesh Shmueli, Oded IBM TJ Watson Res Ctr Yorktown Hts NY 10598 USA Technion Dept Comp Sci IL-32000 Haifa Israel

ISBN: (纸本)9781450356435

Recently, the Artificial Intelligence (AI) field has been experiencing a resurgence. AI broadly covers a wide swath of techniques which include logic-based approaches, probabilistic graphical models, and machine learning/deep learning approaches. Advances in hardware capabilities, such as Graphics processing Units (GPUs), software components (e.g., accelerated libraries, programming frameworks), and systems infrastructures (e.g., GPU-enabled cloud providers) has led to a wide-spread adaptation of AI techniques to a variety of domains. Examples of such domains include image classification, autonomous driving, automatic speech recognition (ASR) and conversational systems (chatbots). AI techniques not only support multiple datatypes (e.g., free text, images, or speech), but are also available in various configurations, from personal devices to large-scale distributed systems.

关键词： Database Systems Artificial Intelligence machine learning Neural Networks

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An Image Compression processing Method Based On Deep learning 2

An Image Compression Processing Method Based On Deep Learnin...

引用

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

作者： Liu Ruihua Zhou Quan Xiao Huachao CAST Natl Key Lab Sci & Technol Space Microwave Xian Peoples R China

ISBN: (纸本)9781728151021

In recent years, the combination of neural network and deep learning has made outstanding achievements in image compression, recognition and other fields. However, the study found that when using a given generative adversarial network (GAN) compression model, the color image compression performance is better than the gray image. In order to improve the compression effect of gray image, a post-processing method is proposed in this paper. The main idea is to process each component of the image generated by the compression model, so as to reduce the influence of different weights of training parameters on the generated image. The method is divided into two cases. One is equal weight processing of component coefficients, which means that the generated component coefficients are all 1/3. The peak signal to noise ratio (PSNR) of images can be improved within the range of 0.10-0.41dB, and the average can be improved by 0.21dB. The other is the unequal weight processing of component coefficients. After data fitting, it is concluded that when the component coefficients are 0.08, 0.49 and 0.35 respectively, the PSNR can be increased within the range of 0.15-1.1dB, and the average can be increased by 0.55dB. Therefore, this method can effectively improve the compression quality of gray image.

关键词： Image Compression Deep learning Neural Network Post-processing

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machine learning Approach for Automatic Weld Defect Detection Using Radiographic Images 2

Machine Learning Approach for Automatic Weld Defect Detectio...

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2nd international conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Edupuganti, Venkata Ramana Penekalapati, Sai Varun Namala, Kiran Kumar VNR Vignana Jyothi Institute of Engineering and Technology Mechanical Engineering Department Hyderabad India

ISBN: (纸本)9798350366570

Radiographic images of weld beads are important to inspect subsurface weld defects for safety assurance. The diversified problems associated with the human analysis of radiographic images of the weldments paved the way to the use of machine learning approach for the detection of subsurface defects. Automatic weld defect inspection involves image pre-processing, defect detection, and classification of defects. The techniques of digitization of radiographs, image pre-processing, defect segmentation and classification are summarized. Advanced methods for radiographic weld defect image classification using machine learning and challenges associated with the current technology are discussed for future improvement. © 2024 IEEE.

关键词： Image segmentation

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Writer Identification using a Probabilistic Model of Handwritten Digits and Approximate Bayesian Computation 2

Writer Identification using a Probabilistic Model of Handwri...

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

作者： Ahmadian, Amirhosein Fouladi, Kazim Araabi, Babak Nadjar Univ Tehran Control & Intelligent Proc Ctr Excellence Sch Elect & Comp Engn Machine Learning & Computat Modeling Lab Tehran Iran

ISBN: (纸本)9781509058204

Writer identification is a classification problem where the classes correspond to a group of writers and the data points are their handwriting samples. This paper proposes an approach to offline text-sensitive writer identification on the basis of a probabilistic generative model of isolated handwritten digits. The model parameters are learned separately for each writer, and the writer of query samples is identified via Bayes decision rule. We deal with a scenario where just a few handwritten instances of a certain Latin digit exist as the training set for each writer. To handle the data sparseness problem, the parameters are learned in a fully Bayesian fashion instead of point estimators through a Markov chain Monte Carlo sampling algorithm. In addition, the hyperparameters are obtained from a background set which contains samples from various writers. Due to computational concerns, an explicit likelihood function is not defined in the model. Instead, the ideas of approximate Bayesian computation have been used in performing inference. Experimental results support the key ideas of the proposed approach.

关键词： Writer Identification Generative Model Bayesian Estimation Bayesian Network Approximate Bayesian Computation Markov Chain Monte Carlo Sampling machine learning Image processing

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SETI (Search for Extra Terrestrial Intelligence) signal Classification using machine learning 2

SETI (Search for Extra Terrestrial Intelligence) Signal Clas...

引用

2nd international conference on Smart Systems and Inventive Technology, ICSSIT 2019

作者： Nanda, Lakshay Santhi, V. VIT SCOPE Vellore India

ISBN: (纸本)9781728121192

In this paper, a literature review on SETI signal spectrogram image classification is presented. Since there has been an abundance of astronomical data, automation seems to be the easier solution for classification and this brings in machine learning into the picture. In this paper, we have discussed both traditional methods and automated methods and also made an analysis of which algorithm comparatively has better performance. We cover classical machine vision along with machine deep learning approach to prototype signal classifiers for extraterrestrial intelligence. Our novel approach uses two-dimensional spectrograms of regular and assumed radio signals taking the establishment of a technological source. The research are executed by applying archived narrow-band signal data taken from real-time SETI observations along the Allen Telescope Array and a set of digitally assumed signals created to mimic real noticed signals. By considering the 2D spectrogram as an image, we exhibit that high quality parametric as well as nonparametric classifiers established on automatic visual analysis can attain high levels of intolerance and efficiency, along with low false-positive rates. © 2019 IEEE.

关键词： Quality control

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Characterizing Mutual Information Loss in Pyramidal Image processing Structures 2

Characterizing Mutual Information Loss in Pyramidal Image Pr...

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

作者： Gibson, Jerry D. Oh, Hoontaek Univ Calif Santa Barbara Elect & Comp Engn Santa Barbara CA 93106 USA

ISBN: (纸本)9781728111988

Gaussian and Laplacian pyramids have long been important for image analysis and compression. More recently, Gaussian and Laplacian pyramids have become an important component of machine learning and deep learning for image analysis and image recognition. Constructing these pyramids consists of a series of filtering, decimation, and differencing operations, and the quality indicator is usually mean squared reconstruction error in comparison to the original image. We present a new characterization of the information loss in a Gaussian pyramid in terms of the change in mutual information. More specifically, we show that one half the log ratio of entropy powers between two stages in a Gaussian pyramid is equal to the difference in mutual information between these two stages. We show that this relationship holds for a wide variety of probability distributions and present several examples of analyzing Gaussian and Laplacian pyramids for different images.

关键词： Entropy Mutual information signal processing Laplace equations Urban areas Image resolution Image coding

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Two Lines Iranian License Plate Detection and Recognition using Subspace learning 2

Two Lines Iranian License Plate Detection and Recognition us...

引用

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

作者： Serajeh, Reza Amnpardaz Software Co Image Proc & Machine Vis Grp Tehran Iran

ISBN: (纸本)9781509058204

License plate detection and recognition has become high-priority research because of its many applications such as electronic payment systems, traffic monitoring and security control. This subject is divided into 2 main parts: license plate detection and character recognition. Although, there have been many studies in detection, two lines license plates such as Iranian motorcycle is less studied. Detection of this kind of license plates is more complicated because of background clutter. In order to solve this problem, this paper uses vertical edge detection and template matching. The following step is character recognition. There are many challenges such as skew, pose variations, blurriness, being dirty, illumination changes and some other distortions in real scenes. Our approach uses subspace learning to have proper performance under these conditions. To show the efficiency of our approach, the database including 60101 digits images is collected and the recognition algorithm is tested on it.

关键词： License plate recognition (LPR) license plate detection license plate segmentation intelligent transportation systems

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Spatio-temporal Analysis and machine learning for Traffic Accidents Prediction 2

Spatio-temporal Analysis and Machine Learning for Traffic Ac...

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2nd international conference on signal processing and Information Security, ICSPIS 2019

作者： Al-Dogom, Diena Aburaed, Nour Al-Saad, Mina Almansoori, Saeed College of Engineering and IT University of Dubai Dubai United Arab Emirates Dubai United Arab Emirates

ISBN: (纸本)9781728138732

Traffic accidents impose significant problems in our daily life due to the huge social, environmental, and economic expenses associated with them. The rapid development in data science, geographic data collection, and processing methods encourage researchers to evaluate, delineate traffic accident hotspots, and to effectively predict and estimate traffic accidents. In this study, traffic accidents dataset that covers United Kingdom for the time period between 2012-2014 is investigated. The methodology consists of extracting features weights, and then using these weights with statistical methods provided in ArcGIS in order to classify accidents according to severity and perform hotspot analysis and severity prediction. The proposed method can be effectively used by different authorities to implement an improved planning and management approaches for traffic accident reduction. Moreover, it can identify and locate road risk segments where immediate action should be considered. © 2019 IEEE.

关键词： machine learning

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