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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11-20 订阅

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ICPRAM 2021 - Proceedings of the 10th international conference on pattern recognition Applications and Methods

ICPRAM 2021 - Proceedings of the 10th International Conferen...

引用

10th international conference on pattern recognition Applications and Methods, ICPRAM 2021

ISBN: (纸本)9789897584862

the proceedings contain 74 papers. the topics discussed include: multi-level feature selection for oriented object detection;demonstrating the vulnerability of RGB-D based face recognition to GAN-generated depth-map injection;speech recognition using deep canonical correlation analysis in noisy environments;capsule networks with intersection over union loss for binary image segmentation;generalized dilation structures in convolutional neural networks;exploring motion boundaries in an end-to-end network for vision-based Parkinson’s severity assessment;converting image labels to meaningful and information-rich embeddings;exploring slow feature analysis for extracting generative latent factors;state tracking in the presence of heavy-tailed observations;and active output selection strategies for multiple learning regression models.

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Target Specific Frequency Selection Criteria for Frequency Diverse Array Radar 10

Target Specific Frequency Selection Criteria for Frequency D...

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10th IEEE international conference on Electronics, computing and Communication Technologies (IEEE CONECCT)

作者： Kulkarni, Venugopal Ray, K. P. Vengadarajan, A. LRDE Bangalore Karnataka India DIAT Pune Maharashtra India

ISBN: (纸本)9798350385939;9798350385922

the frequency diverse array (FDA) radar is based on a new array processing technique having a unique advantage of range-dependent beam formation. the design criteria for frequency components of FDA has been an important area of research. there are several techniques brought out to arrive at an appropriate frequency component set to optimize the performance of the radar. In this aspect, enhancement of Radar cross section(RCS) and detectability of small and nano UAV targets by use of frequency diverse array has not been addressed. thus, a novel approach is presented in this paper for the selection of frequencies that will result in RCS enhancement and facilitate the detection of small targets with a detailed analysis of propeller RCS.

关键词： Beam pattern Conventional phased array Frequency Diverse Array Radar Cross Section

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Laya Estimation for Hindustani Classical Vocals, Devoid of Rhythmic Indicators 10

Laya Estimation for Hindustani Classical Vocals, Devoid of R...

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10th IEEE international conference on Electronics, computing and Communication Technologies (IEEE CONECCT)

作者： Bhatta, Srinivasa Puranika Bharadwaj, Suhas Nagaraj Shadakshari, Sumukh Bhat, Ashwini PES Univ Dept Elect & Commun Bengaluru India

ISBN: (纸本)9798350385939;9798350385922

Laya, also known as Tempo, is an integral part of music. the human body intuitively sways or taps to the tempo while listening to any rhythmic music. Laya depends on two parameters. the first parameter is the number of beats and its structure. the second parameter is the speed. Tempo estimation has previously been achieved for music containing percussive elements. these elements can either be from rhythmic instruments or they can be repetitive melodic sounds. the challenge in Hindustani music, comprising of only vocals, is the lack of any such easily distinguishable rhythmic elements. Hence, existing methods do not deliver satisfactory results. the method proposed in this paper, to extract the tempo has been designed to work exclusively with Hindustani vocal music. this work achieves a minimum accuracy of 95%. the method has been tested for different Taalas, Ragas, and a wide range of tempi.

关键词： pattern Matching MFCCs Tempo Estimation PLP Hindustani Music

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Variable masking pattern-based QR codes for high security 10

Variable masking pattern-based QR codes for high security

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10th IEEE international conference on Electronics, computing and Communication Technologies (IEEE CONECCT)

作者： Pasala, Anjaneyulu Mukherjee, Subhro CMR Inst Technol AI&DS Bangalore Karnataka India Upgrad Bangalore Karnataka India

ISBN: (纸本)9798350385939;9798350385922

QR codes are the de facto standard for the digital transfer of data. At the same time, QR code manipulation has become increasingly prevalent, causing serious fraud. To secure QR codes further, we propose scrambling QR codes using a novel method named Random Masking. Proposed Scrambled QR codes can be detected and read only by secure QR scanners. the Scrambled QR codes cannot be manipulated without removing the random mask using a secret key. If scrambled QR codes are replaced by counterfeit QR codes, the secure scanners fail to scan such QR codes. We observed no performance bottleneck or loss in error correction of QR codes due to random masking. We also made the Scrambled QR codes compatible with the existing hardware used for the generation and scanning of QR codes with a simple software upgrade. Our focus is on securing QR patterns without concerning QR payload.

关键词： Security QR Codes Data Security Encryption

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Mean AR-Local Binary pattern For Facial Expressions Classification Based on KNN Classifier 10

Mean AR-Local Binary Pattern For Facial Expressions Classifi...

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17th INDIACom;2023 10th international conference on computing for Sustainable Global Development, INDIACom 2023

作者： Srilatha, Narubonia Reddy, Lokeswara Computer Science and Engineering Jntua Ananthapur India Computer Science and Engineering Kadapa India

ISBN: (纸本)9789380544472

According to its significance, facial expression recognition (FER) has reached an incredibly prominent position in the field of image processing, beginning in the 1990s and continuing to the present day. Face recognition, feature extraction, and face categorization are the three common steps in FER. In contrast to the various FER systems described for recognizing just part of face expressions, the system presented in this study can identify all eight fundamental facial emotions (neutral, pleased, angry, contempt, surprise, sad, fear, and disgust). We employ the Japanese Female Facial Expression (JAFFE) and the Extended Cohn-Kanade (CK+) datasets to verify our results. the presented methods LBP patterns in this work namely, Asymmetric Region LBP(AR-LBP), Mean Asymmetric Region LBP (Mean AR-LBP), We used GLCM feature extractor technique for feature selection. For the given input image, the GLCM calculates different features factors named Contrast, Correlation, and Homogeneity, Entropy, Mean, variance, and dissimilarity which were results to a feature vector. the findings of the given approach, which employed K-Nearest Neighbor (KNN) to categories facial emotions, are then contrasted with those of the standard LBP pattern. the recognition rate utilizing the provided technique is 96% for the CK + data set and 97% for the JAFFE data set, as shown by the testing results. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Emotion recognition

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Ambient Intelligence Ecosystem for Elderly pattern Detection and Care Using Social Robots 17

Ambient Intelligence Ecosystem for Elderly Pattern Detection...

引用

IEEE Congress on Cybermatics: 17th IEEE international conference on Internet of things, ithings 2024, 20th IEEE international conference on Green computing and Communications, GreenCom 2024, 17th IEEE international conference on Cyber, Physical and Social computing, CPSCom 2024, 10th IEEE international conference on Smart Data, SmartData 2024

作者： Gómez-Ramos, Raúl Duque-Domingo, Jaime Zalama, Eduardo Gómez-García-Bermejo, Jaime ITAP-DISA University of Valladolid Cartif Technological Center Valladolid Spain University of Valladolid ITAP-DISA Valladolid Spain

ISBN: (纸本)9798350351637

this article presents an ambient intelligence system to provide support and security to residents living alone, particularly in supervised domiciles. It confronts the challenges arising from the burgeoning elderly population in Europe, many of whom opt for independent living. the system combines non-intrusive sensors, a social robot, and algorithms geared towards activity monitoring and anomaly detection. Central to its functionality is the monitoring of activities, employing a hierarchical Hidden Markov Model (HMM)-based Human Activity recognition (HAR) system. this system adeptly discerns the Activities of Daily Living (ADLs) performed by users, enabling swift interventions or alerts in instances of emergencies. Furthermore, anomalies such as prolonged periods of inactivity or nocturnal wanderings are promptly identified, triggering requisite responses to ensure user safety and well-being. A key component is the cognitive system, which establishes rules for actions predicated on data gleaned from the HAR module and anomaly detection. this enables interactions with the social robot and interventions within the environment. Validation and performance evaluation in real-world scenarios underscore the system's efficacy, with high precision, recall, and F1Score metrics reported for the HAR module. © 2024 IEEE.

关键词： Ambient intelligence

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Data Mining and Cloud computing for Customer pattern Analysis and Value Maximization 10

Data Mining and Cloud Computing for Customer Pattern Analysi...

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10th international conference on Artificial Intelligence and Robotics, QICAR 2024

作者： Sirjani, Mohammad Sadegh Mousavi, Seyed Amir Sadeghi, Mostafa Department of Computer Ferdowsi University of Mashhad Mashhad Iran Faculty of Computer Engineering Najafabad Branch Islamic Azad University Najafabad Iran

ISBN: (纸本)9798350348873

In the contemporary business landscape, companies are seeking efficient methods to analyze customer behavior and extract actionable insights to foster customer relationships and drive business growth. this paper presents a novel approach that combines the strengths of cloud computing, software engineering, and data mining techniques to analyze customer patterns and optimize the value of the customer life cycle. By employing data mining techniques, including clustering, classification, association analysis, and predictive modeling, businesses can identify customer patterns and behaviors. through the extensive analysis of customer data, organizations can uncover concealed patterns, preferences, and trends, which facilitate informed decisions regarding customer acquisition, retention, upselling, and personalized marketing strategies, ultimately maximizing the value of the customer life cycle. the integration of cloud computing, software engineering, and data mining enables businesses to leverage advanced analytics and extract valuable insights from customer data. this approach enables organizations to gain a comprehensive understanding of customer patterns and behaviors, thereby facilitating targeted marketing campaigns, personalized customer experiences, and improved customer satisfaction. ©2024 IEEE.

关键词： Data mining

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GRC-Net: Grouping Guided R-band Chromosome recognition Network 24

GRC-Net: Grouping Guided R-band Chromosome Recognition Netwo...

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10th international conference on computing and Artificial Intelligence, ICCAI 2024

作者： Xia, Chao Liu, Wei Liu, Zhao Jiang Chen, Bing Yang, Jie Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Shanghai Institute of Hematology State Key Laboratory of Medical Genomics National Research Center for Translational Medicine at Shanghai Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China

ISBN: (纸本)9798400717055

Chromosome recognition is a critical and time-consuming process in karyotyping, especially for R-band chromosomes with poor visualization quality. In this paper, we propose an end-to-end grouping guided R-band chromosome recognition method GRC-Net. GRC-Net serves the chromosome recognition task as the main task and takes the chromosome length grouping task and centromere position grouping task as auxiliary tasks. Two auxiliary modules, Chromosome Length Grouping Module (CLGM) and Centromere Position Grouping Model (CPGM), are designed to extract the task-specific feature and refine the feature map of the main task. A large-scale R-band chromosome dataset with 1735 cases was collected. Experiment results on the 423 testing cases show that the proposed GRC-Net gets the highest accuracy of 96.87%, outperforming the baseline by 2.24%. With grouping task-guided feature extraction, GRC-Net reduces approximately 50% of the inter-group misclassification. the proposed GRC-Net can serve as a general framework for incorporating the domain knowledge into the process of feature learning, meanwhile, a powerful tool to assist clinical chromosome karyotyping. © 2024 ACM.

关键词： Feature extraction

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Machine Learning Approaches for Emotion recognition: A Comparative Study 12

Machine Learning Approaches for Emotion Recognition: A Compa...

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12th international conference on Current Trends in Advanced computing, ICCTAC 2024

作者： Singh, Himanshu Singh, Arushi Chauhan, Priyaj Kamboj, Deepika IT KIET Group of Institution Ghaziabad India CSIT KIET Group of Institution Ghaziabad India

ISBN: (纸本)9798350395808

A key component that has arisen in[10] human-computer interaction, artificial intelligence, and affective computing is the ability to recognize emotions. the objective of the research project is to examine a method that is integrated for the recognition of facial expressions of emotion. Humans transmit their emotions through indirect and non-verbal means most of the time. their sentiments are communicated through these methods. Providing a complete overview that includes an assessment of all the important emotion detection methods that can be found in a single region is the objective of the exposition that is now being presented. the paper represents the first attempt to offer an overview of all the emotion detection models that have been developed for the previous 10 years. the document contains an in-depth analysis of the technique as well as the datasets. For face identification, the technique makes use of the local Binary pattern Histogram (LBPH), Convolutional Neural Networks (CNN) for feature extraction, and the Deep Face model for comprehensive emotion analysis. © 2024 IEEE.

关键词： Local binary pattern

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Utilizing Graph Neural Networks to Detect Abnormal Driving patterns in Vehicular Networks 10

Utilizing Graph Neural Networks to Detect Abnormal Driving P...

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10th international conference on Collaboration and Internet computing

作者： Razzazi, Harir Nait-Abdesselam, Farid Nouiri, Maroua Univ Missouri Kansas City MO 64110 USA Nantes Univ Nantes France

ISBN: (纸本)9798350386714;9798350386707

Abnormal driving presents a significant risk to road safety, impacting both drivers and pedestrians. Meanwhile, vehicular networking has emerged as a crucial technology for enhancing road safety by improving vehicle-to-infrastructure communication. this study leverages vehicular network communications to identify dangerous driving behaviors, such as aggressive or impaired driving. We employ graph-based methods to analyze traffic patterns via network topology, known for its clarity, scalability, and broad applicability. By utilizing cooperative awareness messages (CAMs) alongside a synthetic dataset, we map the topological relationships between vehicles. Our approach uses supervised Graph Neural Networks (GNNs) - specifically Graph Convolutional Network (GCN), Graph Attention Network (GAT), and Graph Sample and Aggregate (GraphSAGE) - to accurately detect abnormal driving behaviors. the experimental results demonstrate that GraphSAGE outperforms GCN and GAT, achieving near-optimal performance with high recall, precision, and F1 scores in identifying these behaviors.

关键词： Graph Neural Networks (GNN) Vehicular Networks Abnormal Driving Detection Traffic pattern Analysis

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