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检索条件"主题词=Deep learning algorithms"

共 230 条记录，以下是171-180 订阅

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Real-Time Traffic Monitoring and Status Detection with a Multi-vehicle Tracking System 5th

Real-Time Traffic Monitoring and Status Detection with a Mul...

引用

5th EAI International Conference on Intelligent Transport Systems (INTSYS)

作者： Wang, Lu Lam, Chan Tong Law, K. L. Eddie Ng, Benjamin Ke, Wei Im, Marcus Macao Polytech Inst Sch Appl Sci Taipa Macao Peoples R China

ISBN: (纸本)9783030976033;9783030976026

With live street videos posted online, the Macao Government provides means to the general public to assess the latest road traffic conditions. After reviewing over these videos, a person may decide to change the travel route from the one he or she initially plans to take. To let road users make decisions better and faster, it would be desirable to design an automated software, being a component of an Intelligent Transport System, which offers proper suggestions to the users instantly upon analyzing all available live videos. In this paper, we propose to create a real-time road traffic condition estimation system. Its design is based on a combination of deep learning algorithms: the YOLOv5, deepSORT, and the Non-Maximum Suppression algorithms. Putting together the YOLOv5 with our proposed two-stage NMS strategy, the improvement on the efficiency of object detection on live videos is noticeable. Our two-stage strategy removes the requirement to manually tune the NMS parameters continuously. With deepSORT, we are able to track moving vehicles, and create motion trajectories, which we can use filtering strategy to assess the latest road traffic conditions. Since different lanes on a road may have different traffic situations, we separate the lanes based on angles and propose to use a lane status score independently for each lane. Through the experimental results, our system design could estimate the traffic status in real-time without requiring any manual parametric adjustments.

关键词： Traffic transport systems Multi-vehicle tracking Road traffic status deep learning algorithms

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Hands-On Convolutional Neural Networks with TensorFlow: Solve computer vision problems with modeling in TensorFlow and Python

引用

2018年

作者： Iffat Zafar Giounona Tzanidou Richard Burton Nimesh Patel

ISBN: (数字)9781789132823

ISBN: (纸本)9781789130331

Learn how to apply TensorFlow to a wide range of deep learning and Machine learning problems with this practical guide on training CNNs for image classification, image recognition, object detection and many computer vision challenges. Key Features • Learn the fundamentals of Convolutional Neural Networks • Harness Python and Tensorflow to train CNNs • Build scalable deep learning models that can process millions of items Book Description Convolutional Neural Networks (CNN) are one of the most popular architectures used in computer vision apps. This book is an introduction to CNNs through solving real-world problems in deep learning while teaching you their implementation in popular Python library - TensorFlow. By the end of the book, you will be training CNNs in no time! We start with an overview of popular machine learning and deep learning models, and then get you set up with a TensorFlow development environment. This environment is the basis for implementing and training deep learning models in later chapters. Then, you will use Convolutional Neural Networks to work on problems such as image classification, object detection, and semantic segmentation. After that, you will use transfer learning to see how these models can solve other deep learning problems. You will also get a taste of implementing generative models such as autoencoders and generative adversarial networks. Later on, you will see useful tips on machine learning best practices and troubleshooting. Finally, you will learn how to apply your models on large datasets of millions of images. What you will learn • Train machine learning models with TensorFlow • Create systems that can evolve and scale during their life cycle • Use CNNs in image recognition and classification • Use TensorFlow for building deep learning models • Train popular deep learning models • Fine-tune a neural network to improve the quality of results with transfer learning • Build TensorFlow models that can scale to large da

关键词： deep learning Artificial Intelligence Machine learning Python DL Frameworks Python deep learning algorithms deep learning Techniques AI Methods Intelligent Programs Intelligent algorithms Computational Intelligence deep learning for Vision

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5G MEC+AI Pathology "Anti-Cancer Guardian": Design of Intelligent Gastric Cancer Auxiliary Diagnosis and Warning Platform for Smart Hospital System 15th

5G MEC+AI Pathology "Anti-Cancer Guardian": Design of Intell...

引用

15th International Conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management (DHM)

作者： Sun, Xin Shu, Xin Hu, Juncheng Mo, Chunbao Guilin Med Univ Guilin 541199 Peoples R China Zhonglin Sci Tech Dev Grp Co Ltd Nanning 530201 Peoples R China

ISBN: (纸本)9783031610622;9783031610639

Purpose: Investigate the technical means of intelligent Gastric Cancer (GC) auxiliary diagnosis and warning, providing treatment guidance for physicians. Methods: The product is developed from a technical perspective, involving algorithm design, software development, and application design. Based on the UNet3+ digital pathology slices auxiliary diagnosis method, it enables precise segmentation of breast cancer pathology slices across the entire field of view, addressing issues such as excessive network complexity, high false positives, and inadequate capture of multi-scale information in existing algorithms. Additionally, utilizing a convolutional neural network-based cancer cell image classification algorithm enables rapid generation of predictive results for pathology slices. Key algorithms in 5G Mobile Edge Computing (MEC) for smart healthcare applications facilitate specific applications of 5G technology in integrated data exchange and device integration within medical consortia, achieving offloading of computational tasks and storage content to MEC nodes. This implementation includes patient physiological indicator warning mechanisms and device management techniques based on MEC nodes. The platform assists physicians in diagnosing more efficiently, aiding patients in earlier recovery, and providing robust support for treatment. Results: The new algorithm accurately segments and predicts entire cancer pathology slices, identifying cancerous regions. Conclusion: The "AI Pathology Anti-Cancer Guardian" intelligent GC auxiliary diagnosis and warning platform can analyze gastroscopy results more quickly and accurately, aiding physicians in diagnosis and treatment.

关键词： Gastric Cancer Auxiliary Diagnosis Early Warning deep learning algorithms

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PREDICTION OF DEFORMATION CAUSED BY LANDSLIDES BASED ON GRAPH CONVOLUTION NETWORKS ALGORITHM AND DInSAR TECHNIQUE 6

PREDICTION OF DEFORMATION CAUSED BY LANDSLIDES BASED ON GRAP...

引用

ISPRS GeoSpatial Conference / Joint 6th Sensors and Models in Photogrammetry and Remote Sensing Conference (SMPR) / 4th Geospatial Information Research (GIResearch) Conference

作者： Khalili, M. A. Guerriero, L. Pouralizadeh, M. Calcaterra, D. Di Martire, D. Univ Naples Federico II Dept Earth Environm & Resource Sci Monte Sant Angelo Campus Naples Italy Allameh Tabatabai Univ Dept Stat Math & Comp Sci Tehran Iran

Around the world, the occurrence of landslides has become one of the greatest threats to human life, property, infrastructure, and natural environments. Despite extensive research and discussions on the spatiotemporal dependence of landslide displacements, there is still a lack of understanding concerning the factors that appear to control displacement distribution in landslides because of their significant variations. This paper implements a Graph Convolutional Network (GCN) to predict displacement following the Moio della Civitella landslide in southern Italy and identify factors that may affect the distribution of movement following the landslide. An interferometric technique, known as permanent scatter interferometry (PSI), has been developed based on Synthetic Aperture Radar (SAR) satellite imagery to derive permanent scatter points that can be used to represent the deformation of landslides. This study utilized the GCN regression model applied to PSs points and data reflecting geological and geomorphological factors to extract the interdependency between paired data points, resulting in an adjacency matrix of the interval [0, 0,8). The proposed model outperforms conventional machine learning and deep learning algorithms such as linear regression (LR), K-nearest neighbors (KNN), Support vector regression (SVR), Decision tree, lasso, and artificial neural network (ANN). The absolute error between the actual and predicted deformation is used to evaluate the proposed model, which is less than 2 millimeters for most test set points.

关键词： Prediction Landslides D-InSAR Graph Neural Networks Machine learning algorithms deep learning algorithms

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End-to-End 3D Face Reconstruction with Expressions and Specular Albedos from Single In-the-wild Images 22

End-to-End 3D Face Reconstruction with Expressions and Specu...

引用

30th ACM International Conference on Multimedia (MM)

作者： Deng, Qixin Le, Binh H. Jin, Aobo Deng, Zhigang Univ Houston Houston TX 77204 USA Elect Arts Redwood City CA USA Univ Houston Victoria Victoria TX USA

ISBN: (纸本)9781450392037

Recovering 3D face models from in-the-wild face images has numerous potential applications. However, properly modeling complex lighting effects in reality, including specular lighting, shadows, and occlusions, from a single in-the-wild face image is still considered as a widely open research challenge. In this paper, we propose a convolutional neural network based framework to regress the face model from a single image in the wild. The outputted face model includes dense 3D shape, head pose, expression, diffuse albedo, specular albedo, and the corresponding lighting conditions. Our approach uses novel hybrid loss functions to disentangle face shape identities, expressions, poses, albedos, and lighting. Besides a carefully-designed ablation study, we also conduct direct comparison experiments to show that our method can outperform state-of-art methods both quantitatively and qualitatively.

关键词： 3D face reconstruction deep learning algorithms specular albedo facial expressions

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CRNet: Corner Recognition from Trajectories based on Convolutional and Recurrent Neural Networks 4

CRNet: Corner Recognition from Trajectories based on Convolu...

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IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)

作者： Sun, Yuchen Wang, Bang HUST Sch Elect Informat & Commun Wuhan Hubei Peoples R China

ISBN: (纸本)9781538693803

Corner recognition plays an important role in trajectory-route matching for fingerprint crowdsourcing in indoor positioning systems. However, the pose diversity and fake corner problem in most practical scenarios could much degrade the recognition performance. This paper deals with the two problems from a deep learning perspective. We design the CRNet consisting of convolutional and recurrent neural networks, which can recognize a corner from pedestrian movement trajectories directly from raw sensor measurements. Field experiments have validated its effectiveness in terms of much improved precision, recall and F1-measure compared with the state-of-theart schemes. As the CRNet does not require manual feature extraction, we envision its wide applications in practical corner recognition scenarios.

关键词： Indoor corner recognition fake corner problem pose diversity problem deep learning algorithms indoor positioning systems

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Smile Recognition Based on deep Auto-Encoders 11

Smile Recognition Based on Deep Auto-Encoders

引用

11th International Conference on Natural Computation (ICNC) / 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Liang, Shufen Liang, Xiangqun Guo, Min Wuyi Univ Sch Informat Engn Jiangmen Peoples R China

ISBN: (纸本)9781467376792

Most of smile recognition methods are based on constrained databases. Thus there are a lot of limitations when applying those algorithms into the real-world smile recognition. For the purpose of improving the accuracy in real-world smile recognition, we conducted our experiments on two databases (GENKI-4K database and our own built database). Depending on deep learning theory, we constructed a new deep model by stacking Contractive Auto-Encoder (CAE) on Contractive Denoising Auto-Encoder (CDAE) to extract useful features. Firstly, we pre-trained a CDAE to extract the feature of the first layer, then the extracted feature were used as input of the next basic model CAE, by pre-training the CAE model, we got more abstract feature, then the feature were used to classification. Experiments showed that our approach was useful for smile recognition. On the other hand, we also explored the influence of different number of training samples.

关键词： Smile Recogniton deep learning algorithms Contractive Auto-Encoder Contactive Denoising Auto-Encoder

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Enhancing Diagnosis Precision in Alcohol Addiction Detection Through CNN Analysis with SMOTE-ENN Data Augmentation 4

Enhancing Diagnosis Precision in Alcohol Addiction Detection...

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4th International Conference on Pervasive Computing and Social Networking (ICPCSN)

作者： Ashif, Mohamed H. Kanaga, E. Grace Mary Karunya Inst Technol & Sci Dept Data Sci & Cybersecur Coimbatore Tamil Nadu India

ISBN: (纸本)9798350386356;9798350386349

Alcoholism is a major concern in modern culture, demanding effective and prompt detection measures to limit its negative consequences. Electroencephalography (EEG) data processing has emerged as a viable option for detecting alcoholism by utilizing the brain's unique electrical patterns. However, EEG signals are complicated and multi-channel, deciphering them is difficult. Previous research has primarily relied on traditional machine learning and statistical methodologies, frequently including handcrafted features to identify subjects as alcoholic or non-alcoholic. deep learning models have gained enthusiasm as computer capabilities and data volumes have increased, suggesting prospective options to improve alcoholism diagnosis precision. This study looks into the efficacy of machine learning algorithms like Naive Bayes, Random Forest, and KNN, as well as deep learning algorithms like CNN and LSTM, when combined with data augmentation techniques to improve the performance of alcohol addiction detection. The research uses two independent datasets -one publicly available and one acquired -and performs rigorous preprocessing approaches, including the elimination of NaN values and duplicates, followed by normalization. Three data augmentation strategies, SMOTE, SMOTE-ENN, and SMOTE-TOMEK, are used to address class imbalances in the datasets. The results of a comprehensive research show significant advances in alcohol addiction detection. In the public dataset, the Random Forest method obtains 96.22% accuracy before augmentation, which rises to 98.67% after augmentation with SMOTE-ENN. Similarly, in the obtained dataset, the CNN model obtains 96% accuracy before augmentation, which increases to 98.97% after augmentation with SMOTE-ENN. These findings highlight the potential of deep learning models, when combined with good data augmentation methodologies, to greatly improve the accuracy and reliability of alcohol addiction diagnosis, providing vital insights for future res

关键词： Alcohol Addiction EEG Machine learning algorithms deep learning algorithms Data Augmentation Techniques SMOTE SMOTE-ENN SMOTE-TOMEK Naive Bayes Random Forest KNN CNN LSTM

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LTE User behavior prediction Via LSTM 2

LTE User behavior prediction Via LSTM

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2nd IEEE Conference on Artificial Intelligence (CAI)

作者： Tanha, Mehrdad Adeel, Muhammad MTN Irancell Data Min & Network Planning Tehran Iran

ISBN: (纸本)9798350354102;9798350354096

In this letter we want to use LSTM-RNN to analyze and predict the behavior of LTE users in mobile network. Predicting the user statistics and data usage on the whole mobile network is going to be studied. Analyzing and predicting the user charging data requests in LTE network and finally, finding abnormal base stations has been predicted via DNN networks. To accomplish that we considered aggregated LTE users under each base station and considered the behavior statistics as a time series. Ultimately, we forecasted the coverage index (CI) as a calculated metric to identify anomalies and determine the count of abnormal sites with fewer I,TE CDRs compared to normal ones using LSTNI on generated time series. This prediction can be used for planning and optimization teams to find and solve any abnormal site level parameters. Proposed method can have both the role of corrective actions on base stations to rectify any telecommunication issue on live network and also predictive action for forecasting network condition in site level scale to increase user satisfaction in advance.

关键词： time series prediction deep learning algorithms LTE users look-back window CDR forecasting smart site planning smart parameter twining

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Estimator performance for shallow and deep models

Estimator performance for shallow and deep models

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International Conference on Image Processing, Computer Vision and Machine learning (ICICML)

作者： Xiong, Aosen Chen, Borong Zhou, Yanmin Meng, Linghua Kang, Yudan Chongqing Univ Dept Nucl Engn Chongqing Peoples R China Harbin Inst Technol Shenzhen Dept Comp Sci & Technol Shenzhen Peoples R China Western Univ Dept Sci London ON Canada Shandong Jiaotong Univ Dept Informat Sci & Elect Engn Jinan Peoples R China Univ Calif Berkeley Berkeley CA 94720 USA

ISBN: (纸本)9781665464680

In recommendation system, users post-click conversion post great challenge for training a recommender because selection bias in data. Bias in recommendation system has become a severe issue for ranking performance. Currently, naive, inverse propensity score and doubly robust estimator have been proposed to address the problem. However, the estimator have not been applied to state of the art deep learning models, which are more realistic in practical settings. In this study, to overcome those limitations, we explored the performance of those estimators on shallow models and deep models, and compared their results on same bunch of models. Extensive experiments shows that doubly robust estimator is more robust than inverse propensity score estimator in deep model.

关键词： recommendation system debias estimator deep learning algorithms doubly robust estimator

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