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检索条件"任意字段=6th IAPR International Conference on Pattern Recognition in Bioinformatics"

共 1869 条记录，以下是121-130 订阅

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Automated Emotion recognition through Graphical Cues on Comics at Character Scale 26th

Automated Emotion Recognition Through Graphical Cues on Com...

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26th international conference on pattern recognition, ICPR 2022

作者： Ruddy, théodose Jean-Christophe, Burie Arnaud, Revel L3i Laboratory SAIL Joint Laboratory La Rochelle Université La Rochelle Cedex 117042 France

ISBN: (纸本)9783031377419

Emotions are psychological reactions to external events. Characters represented in artistic works may manifest emotions in order to replicate credible and human-like behaviors in specific situations. they also provides important hints to better understand the stakes and the tone of story. In comics, markers of emotions can be found in the dialogues or through visual cues specifically drawn by the artists. While automated emotion extraction on textual information is an active research field, few works have addressed this topic through the graphical grammar of comics (and more generally on drawings). In this paper, we propose to review the different visual tools used by artists to convey expressiveness to their characters and how they can be exploited for automated processing. Some of those cues are strongly related to the human body, its representation and mechanisms. Consequently, we propose to study developed methods for those topics on photography or captured videos. then, we suggest contributions that aimed at facilitating the transition between real and drawn domains. © 2023, Springer Nature Switzerland AG.

关键词： Emotion recognition

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Fault feature fusion based on entropy-weighted nuisance attribute projection and orthogonal locality preserving projection 6

Fault feature fusion based on entropy-weighted nuisance attr...

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6th international conference on Electromechanical Control Technology and Transportation (ICECTT)

作者： Yang, Di Lv, Yong Yuan, Rui Dang, Zhang Wuhan Univ Sci & Technol Sch Machinery & Automat Wuhan Peoples R China

ISBN: (数字)9781510650398

ISBN: (纸本)9781510650398;9781510650381

Traditional feature fusion methods are difficult to extract sensitive features related to fault under various operating conditions. A new feature fusion method is proposed in this paper, which uses the combination of entropy-weighted nuisance attribute projection (EWNAP) and orthogonal locality preserving projections (OLPP) to extract new features. the clustering performance of the proposed method is macroscopically described by the scatter plot graph, and the new feature vectors processed by EWNAP-OLPP as the samples put into the back propagation (BP) neural network for fault pattern recognition.

关键词： Entropy-weighted nuisance attribute projection Orthogonal locality preserving projection Feature fusion fault pattern recognition neural network

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A Comprehensive Review of Machine Learning and Deep Learning Techniques for Rice Leaf Disease Classification 6th

A Comprehensive Review of Machine Learning and Deep Learning...

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6th international conference on Computational Intelligence in pattern recognition, CIPR 2024

作者： Tabassum, Tasnim Rafiq, Shifat Ara Zulfiker, Md. Sabab Ritu, Nadia Afrin Biswas, Al Amin Department of CSE Daffodil International University Dhaka Bangladesh Department of CSE Sheikh Hasina University Netrokona Bangladesh Department of Computer Science and Engineering Bangabandhu Sheikh Mujibur Rahman University Kishoreganj Bangladesh Department of CSE Jahangirnagar University Dhaka Bangladesh

ISBN: (纸本)9789819780891

Rice is one of the most significant primary crops and provides a substantial portion of the world’s food for billions of people worldwide. However, several leaf-related diseases pose significant risks to the rice crop’s yield and quality. the traditional approaches to disease identification and classification are frequently arduous and require specialized expertise. the domain of machine learning (ML), deep learning (DL), and the convergence of both of these techniques has emerged as a potential approach that can address this issue and enable immediate and accurate diagnosis of rice leaf disease. Additionally, this study offers a thorough analysis and contrast different approaches, focusing on addressing the initial research questions and identifying potential future possibilities. this study functions as a comprehensive resource for research groups engaged in the development of ML and DL applications specifically targeted at the categorization of rice leaf diseases. In addition to aiding various studies in creating feasible applications based on ML and DL techniques, this review effort will instruct the researchers on how these methods might facilitate the identification of rice leaf disease at an early stage. the insights derived from the study offer useful direction for the advancement of research related to rice leaf diseases. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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23rd international conference on Next Generation Wired/Wireless Networking, NEW2AN 2023, and 16th conference on Internet of things and Smart Spaces, ruSMART 2023

23rd International Conference on Next Generation Wired/Wirel...

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23rd international conference on Next Generation Wired/Wireless Networking, NEW2AN 2023, and 16th conference on Internet of things and Smart Spaces, ruSMART 2023

ISBN: (纸本)9783031609961

the proceedings contain 67 papers. the special focus in this conference is on Next Generation Wired/Wireless Networking. the topics include: Detecting PII Leakage Using DPI and Machine Learning in an Enterprise Environment;deep Learning for Preventing Botnet Attacks on IoT;Design of Multi-polarisation MIMO Antenna for Heterogeneous Smartphone Applications;potential Possibilities of Voice pattern recognition by a Distributed Fiber Optic Sensor;Enhancing Wireless Connectivity through Bayesian-Optimized UAV-BS Positioning and Charging;comparison of Centralized and Federated Machine Learning Techniques for Beamtracking in 5G/6G Systems;exploring the Efficiencies and Vulnerabilities of Smart Door Control Systems: A Systematic Review;System-Level Model for SINR and HPBW Evaluation in 5G mmWave UDN with Location-Aware Beamforming;reinforcement Learning Based Power Allocation for 6G Heterogenous Networks;the Impact of Capacity Averaging in Packet-Level Modeling of 5G NR with Blockage and Micromobility;Interference Mitigation for Reconfigurable Intelligent Surface (RIS)-Aided Non-terrestrial Base Station (NTBS) in NOMA Downlink HetNets;modeling a Digital Avatar of a Car Drivers Based on the Quantification of the Information Environment;A New Blockage Detection Approach for 6G thz Systems;real-Time Anomaly Detection in Network Traffic Using Graph Neural Networks and Random Forest;addressing Security and Privacy Issues in a Smart Environment by Using Blockchain as a Preemptive Technique;the Role of Cryptocurrencies in the Development of Countries and the Fight Against Financial Problems;Designing the UzBCS Lending Platform Network Based on Blockchain Technology and Ensure Transaction Security;decentralized Blockchain Networks and Economic Security: Balancing Scalability and Security Tradeoffs;study of Machine Learning Models for IoT Based Efficient Classroom Usage;transforming Higher Education: A Comprehensive Analysis of Blockchain Technologies and Digitalization.

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23rd international conference on Next Generation Wired/Wireless Networking, NEW2AN 2023, and 16th conference on Internet of things and Smart Spaces, ruSMART 2023

23rd International Conference on Next Generation Wired/Wirel...

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23rd international conference on Next Generation Wired/Wireless Networking, NEW2AN 2023, and 16th conference on Internet of things and Smart Spaces, ruSMART 2023

ISBN: (纸本)9783031609930

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Adaptive Global Gesture Paths and Signature Features for Skeleton-based Gesture recognition 27th

Adaptive Global Gesture Paths and Signature Features for Ske...

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27th international conference on pattern recognition, ICPR 2024

作者： Shi, Dongzi Zhang, Xin Cheng, Jiale Xiong, Tong Ni, Hao South China University of Technology Guangdong Guangzhou510000 China University of North Carolina at Chapel Hill Chapel HillNC27514 United States University College London LondonWC1E6BT United Kingdom

ISBN: (纸本)9783031783531

Gestures exhibit sparse joint variations and different time scales, making local dynamic analysis and global spatio-temporal modeling important. Path signature provides mathematical and dynamic analysis of joint trajectories to assist in spatio-temporal modeling. However, previous methods relied on predefined local spatio-temporal joint trajectories, also known as paths. this limitation makes it challenging to directly capture the dynamics of the entire gesture and adapt to varying scales of gesture changes. In this work, we construct the Adaptive Global Gesture Path and extract its signature features as gesture representations. Specifically, we designed global branch to model the global spatio-temporal variation relationship of joints. the dynamic branch is based on the proposed Motion Guided Cluster Attention Block, which emphasizes joints exhibiting similar motion patterns. Combining two branches, the predicted dynamic and global score can distinguish key joints at different times to construct the Adaptive Global Gesture Path that condensely represents the entire gesture. We conducted experiments on the ChaLearn2013 and WLASL datasets, and achieved the state-of-the-art results with much smaller model size. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Gesture recognition

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Comparative Studies of Stochastic Techniques to Minimize the Cost of Biomass Supply Networks 6th

Comparative Studies of Stochastic Techniques to Minimize the...

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6th international conference on Recent Trends in Image Processing and pattern recognition, RTIP2R 2023

作者： Arya, Adarsh Kumar Gautam, Vineeta Kumar, Adarsh Department of Chemical Engineering Harcourt Butler Technical University Uttar Pradesh Kanpur208002 India Department of Oil Technology Harcourt Butler Technical University Uttar Pradesh Kanpur208002 India School of Computer Science University of Petroleum and Energy Studies Dehradun India INTI International University Nilai Malaysia

ISBN: (纸本)9783031530845

the viability of biomass to be used as a consumer product relies heavily on the cost of a Biomass supply network (BSN) that links biomass producers with biorefineries and, finally, with end customers. the current study aims to establish a cost optimization model to minimize the financial burden of BSN. A MILP model has been established and implemented to reduce the costs of a BSN. A comparatively lesser-used stochastic technique, Ant Colony Optimization (ACO), has been used in the present paper to minimize the cost of BSN. Although the ACO technique has succeeded in other settings, it is seldom tested in the context of BSN. the results from the ACO approach have been compared with another popular stochastic optimization technique called the Non-sorting Genetic Algorithm (NSGA-II). According to empirical research, the ACO approach is the most cost-effective optimization technique to lower BSN-related costs. the management may use the blueprint of the optimization model and techniques to develop cost-cutting measures for BSN. © 2024, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Ant colony optimization

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Face recognition Based on Fusion of Multiple Masks Local Feature Sets Using Wavelet Transform 6

Face Recognition Based on Fusion of Multiple Masks Local Fea...

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6th international conference and Workshops on Recent Advances and Innovations in Engineering (ICRAIE)

作者： Al-Dabagh, Mustafa Zuhaer Nayef Ahmad, Muhammad Imran Anwar, Said Amirul Univ Malaysia Perlis Fac Elect Engn Technol Perlis Malaysia Knowledge Univ Dept Comp Sci Erbil Iraq

ISBN: (纸本)9781665434027

Although face recognition confronts several challenges, it has garnered considerable attention over the last two decades. A new proposal of local directional pattern (LDP) operator for face recognition has been introduced to overcome different challenges confronting face recognition systems. this new proposal is known as Multi-Mask Local Directional pattern (MMLDP), which integrates the advantages of LDP operator depending on Robinson and Kirsch masks into one feature vector to give more discrimination information. Firstly, the LDP operator applies various masks to obtain the face image feature, and after that, methods such as Left-Right fusion, Right-Left fusion, Down-Up fusion, and Up-Down fusion are used to fuse the features. Finally, by employing a support vector machine (SVM), the classification of the feature ensues. the conducted experiments utilized the Yale database, and outcomes show that fusion of the features for various masks based on LDP improved performance with the best classification accuracy of 94.2857% compared to LDP and other conventional approaches for face recognition.

关键词： Face recognition Local Directional pattern Kirsch mask Robinson Mask Wavelet Transform

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Improve word segmentation performance from unknown language by decreasing meaningless segmentation 6

Improve word segmentation performance from unknown language ...

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6th international conference on Computational Intelligence and Intelligent Systems, CIIS 2023

作者： Matsushita, Shinya Takase, Haruhiko Takano, Toshiaki Tomotsugu, Katsuko Graduate School of Engineering Mie University Japan Shizuoka Institute of Science and Technology Japan

ISBN: (纸本)9798400709067

this study discussed text analysis for the preservation of minority languages. Text analysis consists of some steps for the analysis of parsing, etc., but word segmentation is necessary before their advanced analysis. We then focused on word segmentation as the first step in the text analysis. For word segmentation of minority languages without prior knowledge, we considered NPYLM effective, which is an unsupervised method. However, NPYLM caused meaningless segmentation if the training texts were insufficient. Meaningless segmentation is an error where a word is segmented into pieces. In this article, we proposed a method to improve meaningless segmentation. the basic idea of the proposal is that meaningless segmentation is caused by the inappropriate or insufficient growth of words, and we can control the growth of words by selecting words for replacement. We performed simple experiments. the results showed that the proportion of correct words in the obtained word groups is improved: vanilla NPYLM gives about 25%, and the proposed method gives about 50%. thus, the proposed method suppresses meaningless segmentation. © 2023 ACM.

关键词： pattern recognition

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Proceeding - 6th international conference on Information Technology, Information Systems and Electrical Engineering: Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022

Proceeding - 6th International Conference on Information Tec...

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6th international conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2022

ISBN: (纸本)9798350399615

the proceedings contain 141 papers. the topics discussed include: mangrove species identification using deep neural network;tuning improvement of power system stabilizer using hybrid Harris Hawk optimization-equilibrium optimizer algorithm;a binarized systolic array-based neuromorphic architecture with high efficiency;context-aware recommendation system survey: recommendation when adding contextual information;monte Carlo method for map area calculation in wildland fire map management;calibration-free monocular distance estimation performance assessment under influences of environmental conditions;rice factory warehouse layout design with a combination of association rule and dedicated storage methods;torque analysis of V-type interior PMSM for electric vehicle based on FEA simulation;analysis of marketplace social media user engagement by topic;face expression recognition with local ternary pattern images using convolutional neural network and extreme learning machine;and dual-robotic-manipulator collaborative system based on depth image.

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