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检索条件"任意字段=2021 10th International Conference on Computing and Pattern Recognition"

共 1677 条记录，以下是391-400 订阅

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AUTOMATED pattern recognition IN REAL-TIME DRILLING DATA FOR EARLY KARST DETECTION 40

AUTOMATED PATTERN RECOGNITION IN REAL-TIME DRILLING DATA FOR...

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40th ASME international conference on Ocean, Offshore and Arctic Engineering (OMAE)

作者： Maksimov, Danil Loken, Marius Alexander Pavlov, Alexey Sangesland, Sigbjorn Norges teknisk naturvitenskapelige Univ NTNU Trondheim Norway

ISBN: (纸本)9780791885208

Drilling in carbonate formations often poses a real challenge to operators, contractors and service companies. Severe fluid losses, gas kicks and other unwanted situations increase drilling risks. these risks are closely related to drilling through karsts - vugs, cavities and fractures. therefore it is important to detect karsts early enough to avoid drilling into them or;once drilling in a karstification region is detected, to prepare risk mitigating actions. Some geophysical methods can be used for karsts detection, however, they have limitations and cannot guarantee early detection of karsts. One of the recent studies has shown that certain patterns in real-time drilling data can serve as indicators of zones with a higher likelihood of encountering karsts. In this paper, we demonstrate how these patterns can be detected in an automated manner with an adaptive differential filter algorithm. the method has been validated on real drilling data.

关键词： Drilling pattern recognition Risk Fluids Fracture (Materials) Algorithms Fracture (Process) Cavities Filters

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SHAMS-Smart Hospital Assisting Multitasking System 10

SHAMS-Smart Hospital Assisting Multitasking System

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10th IEEE international conference on Intelligent computing and Information Systems, ICICIS 2021

作者： Sadek, Esraa Tarek Mohammed, Moataz Mohammad, Mohammad Essam Lotfy Badr, Nagwa Mohamed, Mohamed Atef Ehab Ahmed, Ahmed Abdelghany, Ahmed Ragab Fcis Ain Shams University Computer Systems Cairo Egypt Fcis Ain Shams University Scientific Computing Cairo Egypt Fcis Ain Shams University Cairo Egypt

ISBN: (纸本)9781665440769

Advancements in robotics and artificial intelligence technologies have added a prominent contribution in healthcare services. Due to the exponential increase in synthetic viruses specifically these days, the Covid-19 pandemic caused a high rate of infection and death of hospitals' staff. SHAMS-Smart Hospital Assisting Multitasking System was proposed as the first line of defense between hospital staff and this epidemic. Using cutting edge technologies, SHAMS utilized robotics, embedded systems, electrical engineering, electronics, computer vision, natural language processing, and software engineering to facilitate its services. SHAMS can autonomously navigate, and sterilize the hospital, recognize hospital staff, understand speech commands, deliver medicines to patient room, and afford services to patients. Moreover, making self-sterilizing. this paper studies the development and the impact of utilizing autonomous robots in the healthcare crisis. this research paper discusses the main modules of SHAMS robot. SHAMS showed its efficiency, reliability, and scalability in the medical domain. SHAMS also paved the way for robotics to be utilized in various fields in the middle east. © 2021 IEEE.

关键词： Robotics

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A machine learning approach of finding the optimal anisotropic SPH kernel 10

A machine learning approach of finding the optimal anisotrop...

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10th international conference on Mathematical Modeling in Physical Sciences, IC-MSQUARE 2021

作者： Marinho, Eraldo Pereira Department of Statistics Applied Mathematics and Computing Avenida 24A 1515 Rio Claro Sao Paulo Brazil

It is presented a machine learning approach to find the optimal anisotropic SPH kernel, whose compact support consists of an ellipsoid that matches with the convex hull of the self-regulating k-nearest neighbors of th... 详细信息

关键词： Anisotropy

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Indian Alphabets and Digits Sign recognition Using Pretrained Model 1

引用

5th international conference on Smart computing and Informatics, SCI 2021

作者： Mistree, Kinjal thakor, Devendra Bhatt, Brijesh C. G. Patel Institute of Technology Uka Tarsadia University Bardoli India Dharmsinh Desai Institute of Technology Dharmsinh Desai University Naidad India

ISBN: (数字)9789811697050

ISBN: (纸本)9789811697043

Deaf community uses sign language as their natural language and also as their first language. Indian sign language (ISL) is primary language for deaf people of India, which they use for interacting with other people. Also, not all deaf are aware with reading and writing which makes communication even harder for them. the automated sign language recognition system comes as necessity when human interpreters are not available in urgent situations. In India, local sign languages are used to ease communication among deaf, which lacks the use of standardized sign language. It is important to note that each country has its own unique sign language. Indian Sign Language Research and Training Center (ISLRTC) has launched the first ISL dictionary that can be used as national sign language of India. this paper proposes an approach that uses a novel dataset created using signs of alphabets and digits by ISLRTC to recognize images of ISL. Moreover, the approach uses transfer learning that gives good accuracy with limited number of ISL signs. We have achieved 97.26% accuracy using MobileNet as pretrained model using our approach on our own dataset. © 2022, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： pattern recognition

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Supervised Learning for Negation Scope Detection n Arabic Texts 10

Supervised Learning for Negation Scope Detection n Arabic Te...

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10th IEEE international conference on Intelligent computing and Information Systems, ICICIS 2021

作者： Mahany, Ahmed Fouad, Mohamed M. Aloraini, Afnan Khaled, Heba Nawaz, Raheel Aljohani, Naif R. Ghoniemy, Said Ain Shams University Faculty of Computer and Information Sciences Cairo Egypt Fujitsu Arabia Ltd Jeddah Saudi Arabia Manchester Metropolitan Universit Faculty of Science and Engineering Manchester United Kingdom King Abdulaziz University Faculty of Computing and Information Technology Jeddah Saudi Arabia

ISBN: (纸本)9781665440769

the research in detecting the negation in Arabic is limited due to the unavailability of Arabic corpora targeting this phenomenon. the negation detection affects a set of subfields in the Arabic Natural Language Processing (ANLP), including sentiment analysis and medical information retrieval. therefore, a corpus is manually annotated with negation for targeting this deficiency in the Modern Standard Arabic (MSA) and Classical Arabic (CA) texts. this corpus is collected from various sources, including King Saud University Corpus of Classical Arabic (KSUCCA) and Wikipedia. It includes texts from various topics, like religion, sports, science, biography, health, technology, education, and history. In addition, we propose a supervised-based learning system for the problem of negation scope detection in Arabic texts. Our system depends on Word2Vec and FastText word embeddings with two different classifiers: the Bidirectional Long Short-Term Memory (BiLSTM) and the Support Vector Machined (SVM) as a baseline system. the results show that one of the FastText-BiLSTM based models achieved a classification accuracy of 93% with F1 score of 89%. the fact that the results of the supervised learning are encouraging further proves the point that the treatment of the negation phenomenon is tractable. © 2021 IEEE.

关键词： Sentiment analysis

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Requirement Elicitation and Prototype Development of an Intelligent Environment to Support People With Early Dementia 17

Requirement Elicitation and Prototype Development of an Inte...

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17th international conference on Intelligent Environments (IE)

作者： Grave, Anne Robben, Saskia Oey, Michel Ben Allouch, Somaya Mohammadi, Masi Eindhoven Univ Technol Smart Architectural Technol Eindhoven Netherlands Amsterdam Univ Appl Sci Digital Life Amsterdam Netherlands HAN Univ Appl Sci Architecture Hlth Arnhem Netherlands

ISBN: (纸本)9781665403467

In an aging population, the prevalence of people with dementia is increasing. Intelligent environments can offer a solution to support people in the early stages of dementia, who often experience problems with maintaining their day-night rhythm. the focus of this work is developing an Intelligent Environment for assisting these people with their daily structure while living independently at home. three co-creation methods with several stakeholder groups were used to elicit requirements and develop a prototype of an Intelligent Environment. Participants were involved in sessions using Mind Maps (N=55), Idea Generation Cards (N=35), and in a multidisciplinary design team with representatives of eight organizations. the result of these sessions was a set of 10 requirements for the Intelligent Environment on the following topics: context-awareness, pattern recognition, adaptation, support, personalization, autonomy, modularity, dementia proof interaction, costs, data and privacy. Based on these requirements, several scenarios were developed to explicate such an environment for supporting people with early dementia. the design team translated the requirements to a real-life prototype of an Intelligent Environment integrated into a demonstration home. the prototype serves as a basis for further development of Intelligent Environments to enable people with dementia to live longer independently at home.

关键词： Intelligent Environments Dementia Ambient Assisted Living Ubiquitous computing Pervasive computing Older Adults Healthcare

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An Efficient Convolutional Neural Network Approach for Facial recognition 10

An Efficient Convolutional Neural Network Approach for Facia...

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10th international conference on Cloud computing, Data Science and Engineering (Confluence)

作者： Mangal, Aayushi Malik, Himanshu Aggarwal, Garima Amity Univ ASET Dept Comp Sci Noida UP India

ISBN: (纸本)9781728127910

Data security being the main concern now a days, has faced a lot of threat in terms of breaching of information which requires immediate attention. Biometrics have served a long-run for this purpose which is a part of Deep Learning. In the recent past, face recognition has become a very important tool for safety and security purposes. this paper presents the application of face recognition technique, making use of Convolutional Neural Network (CNN) with Python and a comparison is drawn between the other techniques such as Principal Component Analysis (PCA), Local Binary pattern (LBP) and K Nearest Neighbour (KNN). Unlike conventional methods, the proposed scheme uses four Convolutional layers with ReLu layers, four pooling layers, a fully connected layer and a Softmax Loss Layer to normalize the probability distribution. the dataset consists of 1500 images with different facial expressions and the model is trained and tested in order to acquire an accuracy using CNN method. Experimental results show that the proposed Neural Network scored an accuracy of 96.96%.

关键词： Facial recognition Deep Learning Face Database Convolutional Neural Networks K Nearest Neighbour Principal Component Analysis Local Binary pattern Histogram

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Multi-stage Fusion for One-Click Segmentation 1

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42nd German conference on pattern recognition, DAGM GCPR 2020 held in parallel with 25th international Symposium on Vision, Modeling, and Visualization, VMV 2020 and 10th Eurographics Workshop on Visual computing for Biology and Medicine, VCBM 2020

作者： Majumder, Soumajit Khurana, Ansh Rai, Abhinav Yao, Angela University of Bonn Bonn Germany Indian Institute of Technology Bombay Mumbai India School of Computing National University of Singapore Singapore Singapore

ISBN: (数字)9783030712785

ISBN: (纸本)9783030712778

Segmenting objects of interest in an image is an essential building block of applications such as photo-editing and image analysis. Under interactive settings, one should achieve good segmentations while minimizing user input. Current deep learning-based interactive segmentation approaches use early fusion and incorporate user cues at the image input layer. Since segmentation CNNs have many layers, early fusion may weaken the influence of user interactions on the final prediction results. As such, we propose a new multi-stage guidance framework for interactive segmentation. By incorporating user cues at different stages of the network, we allow user interactions to impact the final segmentation output in a more direct way. Our proposed framework has a negligible increase in parameter count compared to early-fusion frameworks. We perform extensive experimentation on the standard interactive instance segmentation and one-click segmentation benchmarks and report state-of-the-art performance. © 2021, Springer Nature Switzerland AG.

关键词： Deep learning

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Study on Vision System for Disease Detection Using CNN and LabVIEW of an Agricultural Robot 12th

Study on Vision System for Disease Detection Using CNN and L...

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12th international conference on Soft computing and pattern recognition, SoCPaR 2020 and 16th international conference on Information Assurance and Security, IAS 2020

作者： Nahak, Pradeep Jana, Atanu Pratihar, Dilip Kumar Deb, Alok Kanti Ray, Hena Indian Institute of Technology Kharagpur Kharagpur India Centre for Development of Advanced Computing Kolkata India

ISBN: (纸本)9783030736880

Farmers face a lot of problems, while cultivating agricultural plants. they do not get the desired output in agriculture due to the different types of diseases from which the plants suffer. therefore, it is important to detect the disease(s) of the plant. In this study, a vision-based plant disease detection method has been developed, where convolutional neural network (CNN) model has been used for classification of the plant disease and vision assistant toolbox of LabVIEW software is utilized for disease detection. this vision-based disease detection through a smart camera will be one of the important modules of an intelligent agricultural robot. © 2021, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Neural network models

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Multimodal Semantic Forecasting Based on Conditional Generation of Future Features 42nd

Multimodal Semantic Forecasting Based on Conditional Generat...

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作者： Fugošić, Kristijan Šarić, Josip Šegvić, Siniša University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia

ISBN: (纸本)9783030712778

this paper considers semantic forecasting in road-driving scenes. Most existing approaches address this problem as deterministic regression of future features or future predictions given observed frames. However, such approaches ignore the fact that future can not always be guessed with certainty. For example, when a car is about to turn around a corner, the road which is currently occluded by buildings may turn out to be either free to drive, or occupied by people, other vehicles or roadworks. When a deterministic model confronts such situation, its best guess is to forecast the most likely outcome. However, this is not acceptable since it defeats the purpose of forecasting to improve security. It also throws away valuable training data, since a deterministic model is unable to learn any deviation from the norm. We address this problem by providing more freedom to the model through allowing it to forecast different futures. We propose to formulate multimodal forecasting as sampling of a multimodal generative model conditioned on the observed frames. Experiments on the Cityscapes dataset reveal that our multimodal model outperforms its deterministic counterpart in short-term forecasting while performing slightly worse in the mid-term case. © 2021, Springer Nature Switzerland AG.

关键词： Semantics

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