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共 1490 条记录，以下是151-160 订阅

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3rd International conference on Data, Engineering, and Applications, IDEA 2021

3rd International Conference on Data, Engineering, and Appli...

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3rd International conference on Data, Engineering, and Applications, IDEA 2021

ISBN: (纸本)9789811946868

The proceedings contain 53 papers. The special focus in this conference is on Data, Engineering, and Applications. The topics include: An In-Field Real-Time Automatic Weed Detection Using Deep Learning Techniques;sentiment analysis of Indians Due to Conflict Between India and China at the Actual Line of Control in Galwan Valley, Ladakh;feature Selection Using Information Gain for Software Effort Prediction Using Neural Network Model;a Comprehensive Survey of Web and Mobile Apps for Fishermen;study of Machine Learning Classifiers for Intrusion Detection System;the analysis of Time Series Data;computed Tomography image Processing Methods for Lung Nodule Detection and Classification: A Review;Implementation and Analyzing SURF Feature Detection and Extraction on WANG images Using Custom Bag of Features Model;A Method for Improving Efficiency and Security of FANET Using Chaotic Black Hole Optimization-Based Routing (BHOR) Technique;machine Learning Approaches for image-Based Screening of Cervical Cancer;A Comparative analysis of image Steganography Based on Discrete Wavelet Transform (DWT) and Exploiting Modification Direction (EMD);web Attack Detection Using Machine Learning;pragmatic analysis of Web Service Discovery Models and Architectures from a Qualitative Perspective;An Integrated Semi-Supervised Learning Framework for image Compression Using DCT, Huffman Encoding, and LZW Coding;analyzing the Need for Video Summarization for Online Classes Conducted During Covid-19 Lockdown;local Roughness Binary pattern for Texture Classification;Speech Enhancement Using VAD for Noise Estimation in Compressive Sensing;a Comparative Study on Single image De-Raining Using Convolutional Neural Network;a Hybrid Translation Model for Pidgin English to English Language Translation.

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Texture based Bone Radiograph image analysis for the assessment of Osteoporosis using Hybrid Domain 3

Texture based Bone Radiograph Image Analysis for the assessm...

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3rd International conference on Signal Processing and Communication (ICPSC)

作者： Jolly, Sharan Chaudhary, Harshit Bhan, Anupama Rana, Haneet Goyal, Ayush Amity Univ Dept Elect & Commun Engn Noida Uttar Pradesh India Texas A&M Univ Frank H Dotterweich Coll Engn Dept Elect Engn & Comp Sci Kingsville TX USA

ISBN: (纸本)9781665428644

Finding efficient diagnostic methods for skeletal system disease has been a subject of interest and growth in recent years. One such skeletal system disorder is osteoporosis, which is characterized by differences in bone mass density and structure. Due to which bone becomes more vulnerable to fractures. The use of pattern recognition for estimation of osteoporosis disease from images of radiographs of the bone has been a considerable problem up till now. By assimilating frequency domain and spatial domain features of their 2-D radiographic images, we introduced a new approach to differentiate osteoporotic subjects from normal subjects in this paper.

关键词： image Preprocessing Feature Extraction GLCM Gabor filter PCA SVM ANN k-NN Naive Bayes

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3rd International conference on Communication, Devices and Computing, ICCDC 2021

3rd International Conference on Communication, Devices and C...

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3rd International conference on Communication, Devices and Computing, ICCDC 2021

ISBN: (纸本)9789811691539

The proceedings contain 70 papers. The special focus in this conference is on Communication, Devices and Computing. The topics include: Accurate Prediction of Petermann I and II Spot Sizes of First Higher Order Mode for Kerr Type Nonlinear Graded Index Fiber by a Simple Method;analysis of All-Optical XNOR Gate Using Quantum Dot Semiconductor Optical Amplifier (QDSOA);New COVID-19 Normal: An Experimental Prototype of Smart Face Mask Vending Machine—An Indispensable Kiosk;Arduino and ESP32-CAM-Based Automatic Touchless Attendance System;speech Emotion recognition of Tamil Language: An Implementation with Linear and Nonlinear Feature;radiation-Resilient GaN/InxGa1-xN Multi-junction Solar Cells with Varying in Contents;A Simple but Accurate Mathematical Formalism for Prediction of the Far Field pattern of First Higher Order (LP11) Mode in Kerr Type Nonlinear Step Index Optical Fiber;A Priority Based Routing Protocol to Improve Quality of Service (QoS) in Wireless Body Area Network for COVID and Diabetic Patients;accurate Estimation of Dimensionless Vector and Scalar Propagation Constants for First Higher Order Mode of Kerr Type Nonlinear Graded Index Fiber by Simple Mathematical Formalism;Performance analysis of Berlekamp–Massey-Based KES Block for 3-Byte RS Decoder;a Simple Method for Accurate Prediction of Splice Loss for First Higher-Order Mode of Step-Index Fiber in Presence of Kerr Nonlinearity;issues of Knowledge Management in Deep Web and Its Graph-Based analysis;a Generic Approach for Interpolation and image Fusion to Obtain Pan-Sharpening;fastResponse—A Smart Ambulatory System for Monitoring Accident/Disaster Victims Along with Live Tracking Facility;COVID-19 Data Forecasting Using Soft Computing Technique;Analyze DGS Antenna Structure.

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Enhanced Human Interaction recognition Framework using Pyramid Matching and Deep Neural Network

Enhanced Human Interaction Recognition Framework using Pyram...

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Emerging Trends in Electrical, Control, and Telecommunication Engineering (ETECTE), International conference on

作者： Tanvir Fatima Naik Bukht Ahmad Jalal Hameedur Rahman Faculty of Computing & Artificial Intelligence Air University Islamabad Pakistan

ISBN: (数字)9798331504861

ISBN: (纸本)9798331504878

The interaction of humans on images has been of interest in computer vision and pattern recognition. In fact, interaction recognition has become fertile ground for research in virtually all areas of research. Thus, proposing to use a Deep Neural Network to design an activity recognition system in that case. However, at the initial stages of the system, the integration of hue, saturation, intensity (HSI) color transformation brings clarity of the video frames. Additionally, filtering for noise interferences are used to minimize noise. Multiple Object Tracking (MOT) and statistical methods are used to extract silhouette. In the extraction procedure, we use the Texton maps, ORB (Oriented FAST and Rotated BRIEF), and Spatial pyramid matching (SPM) Method. Using Gray Wolf algorithm, we discriminate features and then identify the most meaningful independent content that describes the data structure. Finally, the DNN takes the last feature and dumps it into the human interaction zone. This approach is tested using the SBU Interaction dataset and achieves a recognition rate of 87% using the experimental method.

关键词： Statistical analysis image color analysis Computational modeling Surveillance Artificial neural networks Feature extraction Market research Real-time systems Object tracking Low latency communication

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Research on tennis video target tracking based on visual analysis 22

Research on tennis video target tracking based on visual ana...

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3rd Asia-Pacific conference on image Processing, Electronics and Computers, IPEC 2022

作者： Xiao, Menglong Zhao, Qianjie Guangdong Business and Technology University Zhaoqing526000 China

ISBN: (纸本)9781450395786

With the advent of the era of artificial intelligence, great changes have taken place in the traditional broadcasting of sports games. As early as the early 20th century, the Hawkeye system appeared in tennis as a referee aid. However, there is currently no technology that can perform semantic analysis of live video of tennis matches. In the field of machine vision, target tracking is one of the most basic and important branches. It includes image processing, machine learning, pattern recognition and other aspects. It is a research direction involving a very wide range of fields. The complexity of the environment where the moving objects are located and the uncertainty of the moving objects themselves bring great challenges to the detection and tracking of moving objects. Traditional detection and tracking methods based on manual feature extraction have poor generalization ability and can not meet the needs of target detection and tracking in complex moving scenes. By estimating the two-dimensional posture of the players in the player area, analyzing their body skeleton and key information, the analysis of the players' movement types is completed. At the same time, according to the displacement information of key points of athletes' bodies and the running time of the system, the movement distance and speed of athletes can be obtained. © 2022 ACM.

关键词： Tennis

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Deep Aesthetic Assessment and Retrieval of Breast Cancer Treatment Outcomes 10th

Deep Aesthetic Assessment and Retrieval of Breast Cancer Tre...

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10th iberian conference on pattern recognition and image analysis (IbPRIA)

作者： Silva, Wilson Carvalho, Maria Mavioso, Carlos Cardoso, Maria J. Cardoso, Jaime S. Univ Porto Fac Engn Porto Portugal INESC TEC Porto Portugal Champalimaud Fdn Breast Unit Lisbon Portugal Nova Med Sch Lisbon Portugal

ISBN: (纸本)9783031048814;9783031048807

Treatments for breast cancer have continued to evolve and improve in recent years, resulting in a substantial increase in survival rates, with approximately 80% of patients having a 10-year survival period. Given the serious that impact breast cancer treatments can have on a patient's body image, consequently affecting her self-confidence and sexual and intimate relationships, it is paramount to ensure that women receive the treatment that optimizes both survival and aesthetic outcomes. Currently, there is no gold standard for evaluating the aesthetic outcome of breast cancer treatment. In addition, there is no standard way to show patients the potential outcome of surgery. The presentation of similar cases from the past would be extremely important to manage women's expectations of the possible outcome. In this work, we propose a deep neural network to perform the aesthetic evaluation. As a proof-of-concept, we focus on a binary aesthetic evaluation. Besides its use for classification, this deep neural network can also be used to find the most similar past cases by searching for nearest neighbours in the high-semantic space before classification. We performed the experiments on a dataset consisting of 143 photos of women after conservative treatment for breast cancer. The results for accuracy and balanced accuracy showed the superior performance of our proposed model compared to the state of the art in aesthetic evaluation of breast cancer treatments. In addition, the model showed a good ability to retrieve similar previous cases, with the retrieved cases having the same or adjacent class (in the 4-class setting) and having similar types of asymmetry. Finally, a qualitative interpretability assessment was also performed to analyse the robustness and trustworthiness of the model.

关键词： Aesthetic evaluation Breast cancer Deep learning image retrieval Interpretability

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A Comparison of Deep Learning Methods for Inebriation recognition in Humans 1

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21st International conference on image analysis and Processing (ICIAP)

作者： Bhango, Zibusiso van der Haar, Dustin Univ Johannesburg Kingsway Ave & Univ Rd ZA-2092 Johannesburg South Africa

ISBN: (数字)9783031064272

ISBN: (纸本)9783031064272;9783031064265

Excessive alcohol consumption leads to inebriation. Driving under the influence of alcohol is a criminal offence in many countries involving operating a motor vehicle while inebriated to a level that renders safely operating a motor vehicle extremely difficult. Studies show that traffic accidents will become the fifth most significant cause of death if inebriated driving is not mitigated. Inversely, 70% of the world population can be protected by mitigating inebriated driving. Short term effects of inebriation include lack of balance, inhibition and fine motor coordination, dilated pupils and slow heart rate. An ideal inebriation recognition method that operates in real-time is less intrusive, more convenient, and efficient. Deep learning has been used to solve object detection, object recognition, object tracking and image segmentation problems. In this paper, we compare deep learning inebriation recognition methods. We implemented Faster R-CNN and YOLO methods for our experiment. We created our dataset of sober and inebriated individuals made available to the public. Six thousand four hundred forty-three (6443) face images were used, and our best performing pipeline was YOLO with a 99.6% accuracy rate.

关键词： Deep learning Computer vision R-CNN YOLO Inebriation recognition Inebriation detection Drunk driving

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Cloud Feature Extraction and Fluctuation pattern recognition Based Ultrashort-Term Regional PV Power Forecasting

Cloud Feature Extraction and Fluctuation Pattern Recognition...

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3rd IEEE Student conference on Electrical Machines and Systems (SCEMS)

作者： Wang, Fei Li, Jianan Zhen, Zhao Wang, Chao Ren, Hui Ma, Hui Zhang, Wei Huang, Lang North China Elect Power Univ Dept Elect Engn Baoding 071003 Peoples R China North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Beijing 102206 Peoples R China North China Elect Power Univ Hebei Key Lab Distributed Energy Storage & Microg Beijing 102206 Peoples R China Inner Mongolia Power Grp Co Ltd Dispatch & Control Ctr Hohhot 010000 Peoples R China Beijing Goldwind Smart Energy Technol Co Ltd Beijing 100176 Peoples R China TBEA Xinjiang Sunoasis Co Ltd Xian 710119 Peoples R China

Regional photovoltaic(PV) power forecasting provides a foundation for grid management and trading in the power markets. To tackle the deficiency of conventional regional PV power modeling methods, such as the problem of challenging to select useful meteorological information and the problem that a single model cannot fully learn the complex and diverse fluctuation characteristics of power curves, an ultrashort-term regional PV power forecasting framework assembled by fusing fluctuation pattern recognition (FPR) and deep learning modeling under a fluctuation pattern prediction (FPP) model was proposed. First of all, the fluctuation characteristics of the regional PV power curves were extracted, including sampling loss area, mean value, standard deviation, and third derivative. Then, a K-means algorithm based FPR model was established to obtained three kinds of patterns. FPP model based on convolutional neural network is used to predict future PV power fluctuation patterns with historical satellite images as input. Finally, the convolutional autoencoder is used to extract cloud distribution features, and a long- and short-term memory network based on the combination of cloud distribution features and historical output is proposed to construct prediction models of the three models. Through the analysis of the simulation data, the prediction method proposed in this article has higher prediction accuracy than the traditional prediction model.

关键词： Predictive models Fluctuations Data models Photovoltaic systems Clouds Satellites Meteorology Fluctuation pattern prediction fluctuation pattern recognition long- and short-term memory network regional photovoltaic power satellite image

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3rd International conference on Recent Trends in image Processing and pattern recognition, RTIP2R 2020

3rd International Conference on Recent Trends in Image Proce...

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3rd International conference on Recent Trends in image Processing and pattern recognition, RTIP2R 2020

ISBN: (纸本)9789811605062

The proceedings contain 78 papers. The special focus in this conference is on Recent Trends in image Processing and pattern recognition. The topics include: Ensemble of Nested Dichotomies for Author Identification System Using Similarity-Based Textual Features;Feature Combination of Pauli and H/A/Alpha Decomposition for Improved Oil Spill Detection Using SAR;a Fast and Efficient Convolutional Neural Network for Fruit recognition and Classification;copy-Move image Forgery Detection Using Discrete Wavelet Transform;a Comprehensive Survey of Different Phases for Involuntary System for Face Emotion recognition;classification of Vehicle Type on Indian Road Scene Based on Deep Learning;indian Road Lanes Detection Based on Regression and clustering using Video Processing Techniques;detection of Emotion Intensity Using Face recognition;double Authentication System Based on Face Identification and Lipreading;fuzzy Approach to Evaluate Performance of Teaching Staff in Technical Institutions;safety Gear Check at Industries and Laboratories Using Convolutional Neural Network Based on Deep Learning;analysis of Changing Trends in Textual Data Representation;detection of Falsary Happening on Social Media Using image Processing: Feature Extraction and Matching;Development of Multi Faces recognition System Using HOG Features and Neural Network Classifier in Real Time Environment;extraction of Key Frame from Random Videos Based On Discrete Cosine Transformation;Prediction of SO2 Air Pollution Quality Parameter of Kolhapur City Using Time Series analysis;A Big Data Prediction for Weather Forecast Using Hybrid ARIMA-ANN Time Series Model;automatic Detection of Riots Using Deep Learning;protecting Big Data Sets from Unauthorized Users on Cloud;text Categorization: A Lazy Learning-Based Approach.

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3rd International conference on Recent Trends in image Processing and pattern recognition, RTIP2R 2020

3rd International Conference on Recent Trends in Image Proce...

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3rd International conference on Recent Trends in image Processing and pattern recognition, RTIP2R 2020

ISBN: (纸本)9789811604928

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