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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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Food Image Categorization Using Attentional Bilinear Model 11

Food Image Categorization Using Attentional Bilinear Model

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

作者： Nussiri, Vasinee Vateekul, Peerapon Chulalongkorn Univ Fac Engn Dept Comp Engn Chulalongkorn Univ Big Data Analyt & IoT Ctr CUBI Bangkok Thailand

ISBN: (纸本)9781728140551

Nowadays, many food images are posted on various social network platforms without identification labels. An automatic food categorization application would greatly help to identify and classify food categories. Food categorization is a complex problem since the number of category types can be more than one hundred. Many kinds of food are similar with only subtle differences in taste and presentation and this can lead to a problem called "fine-grained issue". Recently, a bilinear model was employed which showed good accuracy and generated excessive features to capture details among different food categories, albeit with limited performance. Diverse food categories require disparate sets of features. Here, an attention mechanism was applied to capture suitable features and specifically identify each food category. Furthermore, the performance of a bilinear backbone was also enhanced by applying Inception in correlation with Inception-ResNet-v2 and Inception-v3 networks. the experiment was conducted on the Wongnai dataset containing various images that were separated into 83 classes. Results showed that our attentional model outperformed the traditional bilinear model, with an average of 16% improvement showing 3% and 44% as min-max performance values, respectively.

关键词： Bilinear Convolutional Neural Network Deep learning Attention Mechanism Fine-grained categorization Inception Network

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Public Sector Corruption Analysis with Modified K-means Algorithm Using Perception data

Public Sector Corruption Analysis with Modified K-means Algo...

引用

international conference on Electrical and Computer engineering (ICECE)

作者： Anik Pramanik Amlan Sarker Zabirul Islam M.M.A. Hashem Khulna University of Engineering & Technology (KUET) Khulna Bangladesh

ISBN: (数字)9781665422543

ISBN: (纸本)9781665446617

Corruption in public sectors has received increased attention over the last few decades due to its widespread presence all over the globe. We assert that public sector corruption must be properly analyzed and detected as soon as possible so that preventive and corrective measures can be taken. thus, we developed a model that uses a modified K-means approach, based on K-means algorithm, on perception data, to analyze corruption within the public sector. the model was applied to analyze the corruption within public sectors of Bangladesh. the public sector organizations were segmented based on their different levels of corruption and resultant clustering showed an accuracy of 87.5% with official corruption data as reference. this paper also demonstrates the architecture of a cloud based web-application, developed based on this model that adds robustness and autonomy to the intelligent system.

关键词： Analytical models Sociology Clustering algorithms Organizations data models Robustness Statistics

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Bottleneck Feature Extraction-Based Deep Neural Network Model for Facial Emotion Recognition 10th

Bottleneck Feature Extraction-Based Deep Neural Network Mode...

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10th EAI international conference on Mobile Networks and Management, MONAMI 2020

作者： Ma, Tian Benon, Kavuma Arnold, Bamweyana Yu, Keping Yang, Yan Hua, Qiaozhi Wen, Zheng Paul, Anup Kumar College of Computer Science and Technology Xi’an University of Science and Technology Xi’an710054 China Global Information and Telecommunication Institute Waseda University Shinjuku Tokyo169-8050 Japan Shenzhen Boyi Technology Company Ltd. Shenzhen518125 China Computer School Hubei University of Arts and Science Xiangyang441000 China School of Fundamental Science and Engineering Waseda University Tokyo169-8050 Japan Department of Electronics and Communications Engineering East West University Dhaka1212 Bangladesh

ISBN: (纸本)9783030640019

Deep learning is one of the most effective and efficient methods for facial emotion recognition, but it still encounters stability and infinite feasibility problems for faces of different races. To address this issue, we proposed a novel bottleneck feature extraction (BFE) method based on the deep neural network (DNN) model for facial emotion recognition. First, we used the Haar cascade classifier with a randomly generated mask to extract the face and remove the background from the image. Second, we removed the last output layer of the VGG16 transfer learning model, which was applied only for bottleneck feature extraction. third, we designed a DNN model with five dense layers for feature training and used the famous Cohn-Kanade dataset for model training. Finally, we compared the proposed model with the K-nearest neighbor and logistic regression models on the same dataset. the experimental results showed that our model was more stable and could achieve a higher accuracy and F-measure, up to 98.59%, than other methods. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2020.

关键词： Emotion Recognition

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Feature Extraction with Triplet Loss to Classify Disease on Leaf data

Feature Extraction with Triplet Loss to Classify Disease on ...

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international conference on Awareness Science and Technology (iCAST)

作者： Ty V. Nguyen Incheon Paik Can Tho University Vietnam Intelligent Data Analytics Laboratory The University of Aizu Japan

ISBN: (数字)9781728191195

ISBN: (纸本)9781728191201

this paper addresses the plant disease detection and classification using Deep learning approach. In particular, we propose a novel model using the Triplet Loss together with the fine-tuned pre-trained MobileNet model to extract good features, classify, and detect diseases of plants from the open-source PlantVillage dataset. Using our proposed model, the achievable results are 99.92%, which outperforms the existing models using the same dataset. Furthermore, our proposed model can support the large-scale agricultural sector, which plays an important role in ensuring food security during the current COVID-19 crisis.

关键词： Feature extraction Diseases Support vector machines Computer architecture Computational modeling Radio frequency Training

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PROMISE 2019 - 15th international conference on Predictive Models and data Analytics in Software engineering

PROMISE 2019 - 15th International Conference on Predictive M...

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15th international conference on Predictive Models and data Analytics in Software engineering, PROMISE 2019, co-located with the 13th international Symposium on Empirical Software engineering and Measurement, ESEM 2019

ISBN: (纸本)9781450372336

the proceedings contain 11 papers. the topics discussed include: the technical debt dataset;which refactoring reduces bug rate?;on usefulness of the deep-learning-based bug localization models to practitioners;an evaluation of parameter pruning approaches for software estimation;patterns of effort contribution and demand and user classiï¬ücation based on participation patterns in NPM ecosystem;leveraging change intents for characterizing and identifying large-review-effort changes;prioritizing automated user interface tests using reinforcement learning;reviewer recommendation using software artifact traceability graphs;applying cross project defect prediction approaches to cross-company effort estimation;and from reports to bug-fix commits: a 10 years dataset of bug-fixing activity from 55 apacheâ€™s open source projects.

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Pattern recognition method for mobile intelligent programming 11

Pattern recognition method for mobile intelligent programmin...

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11th international conference on Digital Image Processing, ICDIP 2019

作者： Yu, Bo Guan, Zhenzhi Xia, Tengxi Liu, Yifu School of Computer Science and Technology Harbin University of Science and Technology Harbin150080 China

ISBN: (数字)9781510630765

ISBN: (纸本)9781510630758

Aimed at the problem that there is no efficient program learning software in the education market, leading to many programming problems cannot be solved quickly and accurately, the paper researched and proposed the recognition and analysis algorithms based on OCR (Optical Character Recognition) technology and programming knowledge base. Based on the proposed method, we developed an intelligent mobile software for programming learning. the main function of the software includes code recognition through photographing and programming exercises. After the software unit test and system integration test, it was found that the method can be excellent for code recognition and extraction. And it proved that code assistant software based on recognition analysis algorithm can solve the programming problem quickly and accurately. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Optical character recognition

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Real-Time Visual-Based Localization for Mobile Robot Using Structured-View Deep learning 15

Real-Time Visual-Based Localization for Mobile Robot Using S...

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15th IEEE international conference on Automation Science and engineering (IEEE CASE)

作者： Hong, Yi-Feng Chang, Yu-Ming Li, Chih-Hung G. Natl Taipei Univ Technol Grad Inst Mfg Technol Taipei 10608 Taiwan

ISBN: (纸本)9781728103563

this paper demonstrates a place recognition and localization method designed for automated guidance of mobile robots. Collecting and annotating sufficient images for a supervised deep learning model is often an exhausting work. Devising an effective visual detection scheme for a mobile robot location detection job in a feature-barren environment such as the indoor corridors of buildings is also quite challenging. To address these issues, a supervised deep learning model for the spatial coordinate detection of a mobile robot is proposed here. Specifically, a novel technique is introduced involving structuring and collaging of the surrounding views obtained by the on-board cameras for the training data preparation. A system linking robot kinematics and image processing provides automatic data annotation, which significantly reduces the need for human work on data preparation. Experimental evidence showed that the precision and recall rates of the location coordinate detection are 0.91 and 0.85, respectively. Also, the detection appeared to be effective over a path width of 0.75 m, which is sufficient to cover the possible deviations from the target path. Furthermore, it took averagely 0.14 s for each visual detection performed by an ordinary PC on-board the mobile robot;thus, real-time navigation using the proposed method is achievable.

关键词： Mobile robots Visualization Robot kinematics Cameras Robot vision systems Real-time systems

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Classification Model for Cerebral Aneurysm Rupture Prediction using Medical and Blood-flow-simulation data 11

Classification Model for Cerebral Aneurysm Rupture Predictio...

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11th international conference on Agents and Artificial Intelligence (ICAART)

作者： Suzuki, Masaaki Haruhara, Toshiyuki Takao, Hiroyuki Suzuki, Takashi Fujimura, Soichiro Ishibashi, Toshihiro Yamamoto, Makoto Murayama, Yuichi Ohwada, Hayato Tokyo Univ Sci Dept Ind Adm Chiba Japan Jikei Univ Dept Neurosurg Sch Med Tokyo Japan Jikei Univ Dept Innovat Med Informat Technol Sch Med Tokyo Japan Tokyo Univ Sci Dept Mech Engn Tokyo Japan

ISBN: (纸本)9789897583506

Stroke is a serious cerebrovascular condition, in which brain cells die due to an abrupt blockage of arteries supplying blood and oxygen or due to bleeding in the brain tissue when a blood vessel bursts or ruptures. Because stroke occurs suddenly in most people, prevention is oftentimes difficult. In Japan, this condition is one of the major causes of death, which is associated with high medical cost, especially among the society's aging population. therefore, stroke prediction and treatment is important. Stroke incidences can be avoided by a preventive treatment based on the risk of onset. However, since judgment of the onset risk largely depends on the individual experience and skill of the doctor, a highly accurate prediction method that is independent of the doctor's experience and skill is the focus of this study. the target of prediction for this research is subarachnoid hemorrhage that is part of stroke. Logistic regression and support vector machine that predict cerebral aneurysm rupture by machine learning using combined medical data and cerebral blood-flow-simulation data were employed to analyze 338 cerebral aneurysm samples (35 ruptured, 303 unruptured). SMOTE algorithm solved the imbalance of data, while the SelectKBest algorithm was used to extract important features from the total 70 features obtained from both data. Out of the 27 important features extracted, 40% belonged to the medical data and the remaining 60% were from the blood-flow-simulation data. Using logistic regression as a classification model, we found the sensitivity of 0.64 and the specificity of 0.85. the results validated the possibility of a highly accurate method of cerebral aneurysm rupture prediction by machine learning using engineering information obtained from mechanical simulation.

关键词： Artificial Intelligence Machine learning Medical data Simulation data Computational Fluid Dynamics Stroke Subarachnoid Hemorrhage Cerebral Aneurysm

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A Just-In-Time-learning Based data-driven Method for Valve Failure Prognostics

A Just-In-Time-Learning Based Data-driven Method for Valve F...

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Prognostics and System Health Management (PHM-Jinan), international conference on

作者： Xiaochuan Li David Mba Panagiotis Loukopoulos Faculty of Computing Engineering and Media De Montfort University Leicester UK School of Aerospace Transport and Manufacturing Cranfield University Cranfield UK

ISBN: (数字)9781728151816

ISBN: (纸本)9781728151823

A just-in-time-learning based data-driven prognostic method is presented in this study to handle system nonlinearity due to multiple operating conditions and diversified degradation patterns. the proposed method adopts a just-in-time learning (JITL) scheme to select historical failure cases that demonstrate similar degradation patterns. A data-driven forecasting model called gradient boosting decision tree (GBDT) is employed to extract degradation features from the selected failure cases and predict system remaining useful life (RUL). the effectiveness of the proposed approach was validated on temperature measurements collected from 13 valve failure cases of an industrial reciprocating compressor.

关键词： Valves Degradation Boosting data models Predictive models Regression tree analysis Temperature measurement

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Design of the Convolution Layer Using SoC FPGA and Evaluation of Latency Using a Camera Signal

Design of the Convolution Layer Using SoC FPGA and Evaluatio...

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international Symposium on Soft Computing and intelligent Systems (SCIS)

作者： Ryoki Kamesaka Yukinobu Hoshino System of Engineering Kochi University of Technology Kochi Japan

ISBN: (数字)9781728197326

ISBN: (纸本)9781728197333

In recent years, deep learning has been applied to various problems such as image recognition, voice recognition, and natural language processing, and has achieved results. At the same time, it is used in various applications and fields and is expected to be used in embedded systems such as IoT devices. However, deep learning requires a large computational cost, generally requires a GPU, and it is difficult to implement on small embedded devices. Also, in recent years, FPGAs have been applied to image processing technology fields such as defect inspection of automobiles, security systems, and industrial products. Hardware acceleration is one of the techniques to improve processing speed and it is often used in the field of image processing. In this work, the hardware acceleration for the convolutional neural network was designed compared to software processing. the hardware-based convolutional layer shows high performance than the software-based design. Additionally, these hardware-based modules were implemented on FPGA. In this paper, we show the detail of the architecture and the comparison result of processing time with CPU.

关键词： Field programmable gate arrays Cameras Convolution Graphics processing units Image processing Performance evaluation Hardware design languages

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