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A Multimodal Approach to Image Sentiment Analysis 20th

A Multimodal Approach to Image Sentiment Analysis

引用

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

作者： Gaspar, Antonio Alexandre, Luis A. Univ Beira Interior Inst Telecomunicacoes Rua Marques d Avila e Bolama P-6201001 Covilha Portugal

ISBN: (纸本)9783030336073;9783030336066

Multimodal sentiment analysis is a process for the classification of the content of composite comments in social media at the sentiment level that takes into consideration not just the textual content but also the accompanying images. A composite comment is normally represented by the union of text and image. Multimodal sentiment analysis has a great dependency on text to obtain its classification, because image analysis can be very subjective according to the context where the image is inserted. In this paper we propose a method that reduces the text analysis dependency on this kind of classification giving more importance to the image content. Our method is divided into three main parts: a text analysis method that was adapted to the task, an image classifier tuned with the dataset that we use, and a method that analyses the class content of an image and checks the probability that it belongs to one of the possible classes. Finally a weighted sum takes the results of these methods into account to classify content according to its sentiment class. We improved the accuracy on the dataset used by more than 9%.

关键词： Multimodal sentiment analysis Image Text Deep learning

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Forecasting thailand's Precipitation with Cascading Model of CNN and GRU 11

Forecasting Thailand's Precipitation with Cascading Model of...

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11th international conference on Information Technology and Electrical engineering (ICITEE)

作者： Manokij, Fuenglada Sarinnapakorn, Kanoksri Vateekul, Peerapon Chulalongkorn Univ Big Data Analyt Bangkok Thailand Chulalongkorn Univ Fac Engn Dept Comp Engn LoT Ctr CUBIC Bangkok Thailand Minist Higher Educ Sci Res & Innovat Hydroinformat Inst Publ Org Bangkok Thailand

ISBN: (纸本)9781728140551

Precipitation prediction is necessary to use in water management, especially in thailand, it can be applied for various ways, such as flood warning, agriculture planning, etc. there are many prior attempts to forecast rainfall from the rain-gauge station. Some deployed traditional machine learning approaches: ARIMA, k-NN, etc. Recently, deep learning approach has shown promising results in this task. However, the accuracy is still limited since the raining period throughout the year in thailand is very scarce, so most rainfall amount is zero. In this paper, we propose to cascade two deep learning networks to tackle this problem: one is a classification model to classify whether it rains or not, and the other is a regression model to predict rainfall amount. Our network is a combination of CNN and GRU, where CNN aims to capture relationship between various sensors and GRU aims to capture time-series information. Furthermore, we also perform multi-step forecasting by applying a rolling mechanism that uses the predicted rainfall and involved features to predict the next 6 steps. the experiment was conducted on hourly rainfall dataset for 6 years (2013-2018) provided from the public government sector in thailand. We use RMSE as performance metric to evaluate three periods of rainfall: Overall, Rain, and non-rain periods and the results show that our cascading model is the winner with only 4.53% in term of RMSE which is the average percentage of difference from over all regions.

关键词： Precipitation prediction Deep learning Convolutional neural network Gated Recurrent Unit

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Rebar detection using ground penetrating radar with state-of-the-Art convolutional neural networks 9

Rebar detection using ground penetrating radar with state-of...

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9th international conference on Structural Health Monitoring of intelligent Infrastructure: Transferring Research into Practice, SHMII 2019

作者： Ahmed, Habib La, Hung Manh Gucunski, Nenad Lab Department of Computer Science and Engineering University of Nevada RenoNV89557 United States Department of Civil and Environmental Engineering Rutgers the State of University of New Jersey PiscatawayNJ08854 United States

Nondestructive Evaluation (NDE) of civil infrastructure has been an active area of research for the past few decades, since traditional inspection of civil infrastructure, mostly relying on visual inspection, is time-consuming, labor-intensive and often provides subjective results. To facilitate this process, different sensors and techniques have been used to effectively carry out this task in an automated fashion. the purpose of this research is to provide a novel learning-based method for detection of steel rebars in reinforced concrete bridge elements. the data from Ground Penetrating Radar (GPR) collected from actual bridges has been used for the development and testing of the novel method. the method utilizes one of the variants of the Convolution Neural Networks (CNNs), namely the Deep Residual Network (ResNet-50). the findings are highlighted through a comparison of the effectiveness in rebar detection with some of the recent works. the results further emphasize the efficacy of Deep learning-based methods for NDE, both in general and in rebar detection by GPR. Copyright © SHMII 2019. All rights reserved.

关键词： Ground penetrating radar systems

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Research on Monitoring Networking and Information Collection Technology of Hazardous Chemicals Based on Converged Communication

Research on Monitoring Networking and Information Collection...

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

作者： Jixing Yang Nan Zhao Yudong Fang Ministry of Emergency Management Communication Information Center Beijing China

ISBN: (数字)9781728165790

ISBN: (纸本)9781728165806

In order to improve the ability of preventing and defusing the major safety risks of hazardous chemicals, strengthen the dynamic monitoring of hazardous chemicals, this paper studies the integration of OPC protocol, Modbus communication protocol and other multi-mode and multi-protocol information collection methods according to the production status of hazardous chemicals enterprises. the Internet of things technology was fully used. this article combines information security and video intelligent analysis functions to achieve accurate, real-time, dynamic, and safe collection of video data, IoT data, system abnormal alarm data, and violation data in the entire process of hazardous chemical production and finally realize wide-area perception and smart monitoring.

关键词： Protocols Information security Production Streaming media Chemicals Monitoring Software engineering

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Enabling robot selective trained deep neural networks for object detection through intelligent infrastructure 2019

Enabling robot selective trained deep neural networks for ob...

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4th international conference on Automation, Control and Robotics engineering, CACRE 2019

作者： Poss, Christian Irrenhauser, thomas Prueglmeier, Marco Goehring, Daniel Zoghlami, Firas Salehi, Vahid Ibragimov, Olimjon Logistics Robotics BMW Group Munich Germany Dahlem Center for Robotics and Machine Learning Freie Universität Berlin Germany Department of Applied Sciences and Mechatronic University of Applied Sciences Munich Germany School of International Business and Entrepreneurship Steinbeis University Berlin Germany

ISBN: (纸本)9781450371865

To save costs in logistics, handling steps are going to be automated by robots in the future. Due to the complex industrial conditions prevailing there, this is only possible with a sufficient degree of intelligence in the respective systems. Despite advances in artificially intelligent algorithms, the latest neural networks reveal significant weaknesses in performance and transferability to other applications. In order to enable a holistic autonomous material flow, the paper presents an infrastructure concept, which makes it possible to identify and train the most suitable networks robot-selectively with very limited effort. Using two practical examples, the functionality of the designed algorithms for the industrial implementation of a new use case as well as the updating and improvement of an existing system is finally outlined. It will be shown that with measures such as the automated collection of training data, the AI-supported labeling process, the intuitive validation of the trained networks via a mobile application and the automated retraining of robots already integrated, a further step can be taken towards holistically automated logistics process chains. © 2019 Association for Computing Machinery.

关键词： Object detection

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Column-based Decoder of Internal Prediction Representation in Cortical learning Algorithms

Column-based Decoder of Internal Prediction Representation i...

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international Symposium on Soft Computing and intelligent Systems (SCIS)

作者： Takeru Aoki Keiki Takadama Hiroyuki Sato The University of Electro-Communications Tokyo

ISBN: (数字)9781728197326

ISBN: (纸本)9781728197333

this work proposes a decoder of internal prediction representation in the cortical learning algorithm (CLA). CLA is a time-series data prediction algorithm. CLA receives an input bit-string at each time-step and encodes it to an internal data representation based on the relationship between the input data bits and columns, which are components of the CLA predictor. To decode the internal predictive presentation to the same format of the input bit-string, the conventional CLA employs the sparse distributed representations classifier (SDRC), which uses the relationship between the input data bits and cells, which are other components of the CLA predictor. To build the relationship between the input data bits and the cells, the conventional SDRC additionally needs a time-consuming learning process and a learning parameter, and the prediction accuracy cannot be expected during its learning process. In order to overcome these problems of the SDRC decoder in the conventional CLA and improve the prediction accuracy, the proposed method decodes the prediction representation inside CLA by utilizing the relationship between the input data bits and the columns built during the encoding process. the proposed method avoids the additional learning process to build the relationship between the input data bits and the cells. Also, the proposed method avoids the setting of any learning parameter needed in the conventional SDRC decoder. Experimental results using artificial benchmark and real-world electricity load time-series data show the proposed column-based decoder contributes to rapidly decreasing the prediction error and achieving smaller prediction error than the conventional SDRC decoder.

关键词： Decoding Synapses Prediction algorithms Encoding Task analysis Stability criteria Distributed databases

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Simulation Framework for Performance Evaluation of HfZrOx Based Ferroelectric Synaptic Devices

Simulation Framework for Performance Evaluation of HfZrOx Ba...

引用

international conference on Electrical and Computer engineering (ICECE)

作者： Tonushree Dutta Md. Kawsar Alam Bangladesh University of Engineering and Technology Dhaka Bangladesh

ISBN: (数字)9781665422543

ISBN: (纸本)9781665446617

We report a numerical device simulation framework for 32-levels conductance based metal-ferroelectric-insulator-semiconductor (MFIS) field effect transistor (FET) synaptic devices. In this regard, we theoretically investigate the behavior of HfZrO x (HZO) as the ferroelectric layer of the MFISFET and obtain its conductance characteristics. Current and conductance levels are obtained from the remnant polarization states which is modeled using a metal-ferroelectric-metal (MFM) capacitor model. the simulation results are in good agreement with reported experimental data. then, the simulation data are used in an artificial neural network to analyse its synaptic behaviour, learning capability and accuracy for pattern recognition applications. the pattern recognition accuracy is determined by a multi layer perceptron (MLP) neural network. A learning accuracy of ~ 85%, similar to the result obtained from experimental data, verifies the used models and simulation framework.

关键词： Performance evaluation Computational modeling Field effect transistors data models Numerical models Pattern recognition Mathematical model

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A Clustering-Based Patient Grouper for Burn Care 20th

A Clustering-Based Patient Grouper for Burn Care

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

作者： Onah, Chimdimma Noelyn Allmendinger, Richard Handl, Julia Yiapanis, Paraskevas Dunn, Ken W. Univ Manchester Manchester Lancs England Med Data Solut & Serv Manchester Lancs England Univ Hosp South Manchester Wythenshawe England

ISBN: (纸本)9783030336172;9783030336165

Patient casemix is a system of defining groups of patients. For reimbursement purposes, these groups should be clinically meaningful and share similar resource usage during their hospital stay. In the UK National Health Service (NHS) these groups are known as health resource groups (HRGs), and are predominantly derived based on expert advice and checked for homogeneity afterwards, typically using length of stay (LOS) to assess similarity in resource consumption. LOS does not fully capture the actual resource usage of patients, and assurances on the accuracy of HRG as a basis of payment rate derivation are therefore difficult to give. Also, with complex patient groups such as those encountered in burn care, expert advice will often reflect average patients only, therefore not capturing the complexity and severity of many patients' injury profile. the data-driven development of a grouper may support the identification of features and segments that more accurately account for patient complexity and resource use. In this paper, we describe the development of such a grouper using established techniques for dimensionality reduction and cluster analysis. We argue that a data-driven approach minimises bias in feature selection. Using a registry of patients from 23 burn services in England and Wales, we demonstrate a reduction of within cluster cost-variation in the identified groups, when compared to the original casemix.

关键词： Patient casemix Clustering data driven

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Design and Development of an Automatic Blood Detection System for Capsule Endoscopy Images 20th

Design and Development of an Automatic Blood Detection Syste...

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

作者： Pons, Pedro Noorda, Reinier Nevarez, Andrea Colomer, Adrian Pons Beltran, Vicente Naranjo, Valery Univ Politecn Valencia I3B Valencia Spain Univ Politecn Valencia ITEAM Res Inst Valencia Spain Hosp Univ & Politecn La Fe Unidad Endoscopia Digest Digest Endoscopy Res Grp IIS La FE Valencia Spain

ISBN: (纸本)9783030336172;9783030336165

Wireless Capsule Endoscopy is a technique that allows for observation of the entire gastrointestinal tract in an easy and non-invasive way. However, its greatest limitation lies in the time required to analyze the large number of images generated in each examination for diagnosis, which is about 2 h. this causes not only a high cost, but also a high probability of a wrong diagnosis due to the physician's fatigue, while the variable appearance of abnormalities requires continuous concentration. In this work, we designed and developed a system capable of automatically detecting blood based on classification of extracted regions, following two different classification approaches. the first method consisted in extraction of hand-crafted features that were used to train machine learning algorithms, specifically Support Vector Machines and Random Forest, to create models for classifying images as healthy tissue or blood. the second method consisted in applying deep learning techniques, concretely convolutional neural networks, capable of extracting the relevant features of the image by themselves. the best results (95.7% sensitivity and 92.3% specificity) were obtained for a Random Forest model trained with features extracted from the histograms of the three HSV color space channels. For both methods we extracted square patches of several sizes using a sliding window, while for the first approach we also implemented the waterpixels technique in order to improve the classification results.

关键词： Wireless capsule endoscopy Blood detection Machine learning Hand-crafted features Deep learning Convolutional neural networks

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Overview and investigation of algorithms for the information bottleneck method 11

Overview and investigation of algorithms for the information...

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11th international ITG conference on Systems, Communications and Coding, SCC 2017

作者： Hassanpour, Shayan Wübben, Dirk Dekorsy, Armin Department of Communications Engineering University of Bremen Bremen28359 Germany

ISBN: (纸本)9783800743629

Lossy data compression has been studied under the celebrated Rate-Distortion theory which provides the compression rate in order to quantize a signal without exceeding a given distortion measure. Recently, with information bottleneck an alternative approach has been emerged in the field of machine learning. the fundamental idea is to include the original source into the problem setup when quantizing an observation variable and to use strictly information theoretic measures to design the quantizer. this paper yields an insight to this framework, discusses corresponding algorithms and their performance, and provides a new algorithmic approach of low complexity. © VDE Verlag GMBH. Berlin. Offenback. All rights reserved.

关键词： Signal distortion

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