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检索条件"任意字段=12th International Conference on Computing and Pattern Recognition, ICCPR 2023"

共 284 条记录，以下是61-70 订阅

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Research on Improved Faster R-CNN in Stacked Artifact recognition 12

Research on Improved Faster R-CNN in Stacked Artifact Recogn...

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12th international conference of Information and Communication Technology, ICTech 2023

作者： Han, Weiguang Han, Xuesong University of Chinese Academy of Sciences Beijing100049 China Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang110168 China

ISBN: (纸本)9798350332919

In order to improve the sorting efficiency of workpieces, improve the sorting quality, reduce the labor intensity of workers and improve the working environment, and at the same time meet the needs of small batch, multi-variety and mixed production, this paper studies the improvement of Faster R-CNN for the field of robotic sorting. Application in stacked workpiece recognition. this paper improves the Faster R-CNN and proposes the Bin-Soft-NMS algorithm based on the Soft-NMS algorithm. the dynamic threshold is used to improve the low recall rate of the model and the low score of the adjacent stacked workpiece bounding box caused by the inaccurate threshold. the problem of inhibition. A stacked workpiece dataset was made by using four kinds of workpieces, and the model was trained on the dataset to complete the experiment. the experimental results show that the average detection accuracy of the improved model in the stacked workpiece scene is 86.26%, and the improvement in mild, moderate and severe scenarios is improved respectively. increased by 1.0%, 3.2% and 4.825%, with an average increase of 3%. © 2023 IEEE.

关键词： Bin-Soft-NMS Faster R-CNN Soft-NMS Stacked artifacts Workpiece identification

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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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Novel Supervised Feature Extraction Algorithm Based on Iterative Calculations

Novel Supervised Feature Extraction Algorithm Based on Itera...

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12th IEEE international conference on Information Reuse and Integration (IRI)/1st international Workshop on Issues and Challenges in Social computing (WICSOC)

作者： Takeuchi, Yohei Ito, Momoyo Kashihara, Koji Fukumi, Minoru Univ Tokushima Grad Sch Adv Technol & Sci Tokushima 770 Japan

ISBN: (纸本)9781457709661

In pattern recognition, the principal component analysis (PCA) is one of the most famous feature extraction methods for dimensionality reduction of high-dimensional datasets. Furthermore, Simple-PCA (SPCA) which is a faster version of the PCA, has been carried out effectively by iterative operated learning. However, in SPCA, when input data are distributed in a complex way, SPCA might not be efficient because it is learned without class information of the dataset. thus, SPCA cannot be said that it is optimal for classification. In this paper, we propose a new learning algorithm, which is learned with the class information of the dataset. Eigenvectors spanning eigenspace of the dataset are obtained by calculation of data variations belonging to each class. We will show the derivation of the proposed algorithm and demonstrate some experiments to compare the SPCA with the proposed algorithm by using UCI datasets.

关键词： pattern recognition principal component analysis supervised learning

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Complex Activity recognition Using Polyphonic Sound Event Detection 12th

Complex Activity Recognition Using Polyphonic Sound Event De...

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12th international conference on Innovative Mobile and Internet Services in Ubiquitous computing (IMIS)

作者： Kang, Jaewoong Kim, Jooyeong Kim, Kunyoung Sohn, Mye Sungkyunkwan Univ Dept Ind Engn Suwon South Korea

ISBN: (纸本)9783319935546;9783319935539

In this paper, we propose a method for recognizing the complex activity using audio sensors and the machine learning techniques. To do so, we will look for the patterns of combined monophonic sounds to recognize complex activity. At this time, we use only audio sensors and the machine learning techniques like Deep Neural Network (DNN) and Support Vector Machine (SVM) to recognize complex activities. And, we develop the novel framework to support overall procedures. through the implementation of this framework, the user can support to increase quality of life of elders'.

关键词： Complex activity pattern recognition Polyphonic sound event detection Machine learning

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Categorical Boosting Machine for Tamil Character recognition Using Shape Based Features 7

Categorical Boosting Machine for Tamil Character Recognition...

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2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： T.m, Vishnu Mukundan Nevatia, Isha Mishra, Tusar Kanti School of Computer Science & Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350304268

Tamil is one of the world's earliest surviving languages, from which modern Indian scripts draw inspiration. However, there has been limited development of Optical Character recognition techniques for Tamil characters. this paper describes a model for Tamil vowels-based Optical Character recognition. the strategy seeks to utilise a CHFR feature extraction technique to extract three distinct feature vectors from the contours of each individual character's centroid. these feature vectors are condensed into a single vector before being sent to the CatBoost classifier for classification. Using the Optuna framework, hyperparameter optimisation and comparative analysis of existing models are performed. the proposed scheme is applied to a dataset of 6,972 handwritten samples evenly divided into 12 classes representing the vowels of the Tamil alphabet. the primary evaluation metric is accuracy, and 84.36% testing accuracy is observed. © 2023 IEEE.

关键词： CatBoost CHFR feature extraction handwritten character recognition OCR pattern recognition Tamil characters

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Named Entity recognition Based on Boundary Enhanced for Chinese Electronic Medical Records 12

Named Entity Recognition Based on Boundary Enhanced for Chin...

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12th international conference of Information and Communication Technology, ICTech 2023

作者： Chen, Xueliang Liu, Tianming Tang, Gongzheng Liu, Yunjiang Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Shandong University Laboratory of Big Data and Artificial Intelligence Technology Beijing China

ISBN: (纸本)9798350332919

With the continuous popularization of medical informatization, the number of medical documents, medical guides, electronic medical records and other medical texts shows a trend of rapid growth and huge scale. Medical texts contain great social value. However, most medical texts exist in an unstructured form and cannot be directly applied to products. therefore, how to fully explore the potential valuable information has become a hot research direction in the field of natural language processing. Named entity recognition is the basic task of natural language processing, and also the key task of medical data mining and analysis. It also lays the foundation for downstream tasks such as medical term relationship extraction and medical term standardization. Named entity recognition aims to find some key phrases in a large number of texts, such as people's names, place names, diseases and drugs. At present, named entity recognition has made great achievements in the open field, but there are still many problems in the research of named entity recognition in the medical field, such as the strong professionalism of entities, the long length of some types of entities, and the existence of complex entities mixed in Chinese and English. Aiming at the problem that the length of named entities in existing medical electronic medical records is longer than that in other fields, resulting in low extraction accuracy, a named entity recognition algorithm based on boundary enhancement is proposed. the model adopts the method of multi-task learning, which can effectively alleviate the boundary problem caused by the length of medical terms. We have tried various models on our medical electronic medical record data set. Experimental results show that this method is simple and effective in medical named entity recognition. © 2023 IEEE.

关键词： Data mining

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DeepPRS: A Deep Learning Integrated pattern recognition Methodology for Secure Data in Cloud Environment 13th

DeepPRS: A Deep Learning Integrated Pattern Recognition Meth...

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13th international conference on Innovations in Bio-Inspired computing and Applications, IBICA 2022, and 12th World Congress on Information and Communication Technologies, WICT 2022

作者： Remesh Babu, K.R. Saritha, S. Preetha, K.G. Unnikrishnan, Sangeetha Izudheen, Sminu Government Engineering College Kerala Idukki India Rajagiri School of Engineering and Technology Kerala Kochi India Government Engineering College Kerala Palakkad India

ISBN: (纸本)9783031274985

the distributed data environment in the cloud poses many security and privacy challenges. the cloud environments and the Intrusion Detection System (IDS) for the same should work with low latency and high efficacy. this paper proposes a Deep learning integrated pattern recognition methodology for Secure (DeepPRS) data in cloud environment. the proposed methodology works in two phases, (i) the first phase runs a single pattern matching algorithm named as PRS algorithm that takes as input – data supplied by the centralized controller, attack history, feature data and detect patterns (ii) the second phase which runs a deep learning model to label the detected pattern as attack or non-attack type. the performance analysis of the proposed algorithm is compared with the existing pattern matching algorithms such as KMP (Knuth-Morris-Pratt Algorithm) and BM (Boyer-Moore).the experimental results shows that the performance of the PRS algorithm is highly appreciable in terms of latency. the detected pattern from the PRS algorithm is fed into a Deep Learning model to classify the type of pattern as either attack or non-attack kind. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Big data

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A New Electronic Voting System Based on Elliptic Curve in a Cloud computing Environment 12th

A New Electronic Voting System Based on Elliptic Curve in a ...

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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： Semmouni, Meryem Cherkaoui Azougaghe, Ali Nitaj, Abderrahmane Belkasmi, Mostafa ICES Team ENSIAS Mohammed V University in Rabat Rabat Morocco LMNO University of Caen Normandie Caen France

ISBN: (纸本)9783030736880

the process of democratization is developing and promoting due to electronic voting. However, the increase of frauds and the increasing amount of attacks launched by hackers, gave birth to privacy and authentication problems. Cryptography offers multiple solutions to overcome the sensitive data protection issues in e-voting. In this paper, we study the application of elliptic curve cryptography, and use the homomorphic encryption properties to present a new electronic voting system. Our new scheme is based on the homomorphic cryptosystem EC-ELGAMAL [7], and zero knowledge algorithm of Schnorr algorithm for identification and authentication. © 2021, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Personal computing

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Fine-Grained and Complex Food Entity recognition Benchmark for Ingredient Substitution 23

Fine-Grained and Complex Food Entity Recognition Benchmark f...

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12th ACM international conference on Knowledge Capture, K-CAP 2023

作者： Lawrynowicz, Agnieszka Wróblewska, Anna Kaliska, Agnieszka Pawlowski, MacIej Wiśniewski, Dawid Sosnowski, Witold Dutkiewicz, Jakub Faculty of Computing and Telecommunications Poznan University of Technology Poznan Poland Faculty of Mathematics and Information Science Warsaw University of Technology Warsaw Poland Faculty of Modern Languages and Literatures Adam Mickiewicz University Poznań Poland Polish-Japanese Academy of Information Technology Warsaw Poland

ISBN: (纸本)9798400701412

Food computing is currently fast-growing into an innovative area of knowledge extraction. However, benchmarks for information extraction from semi-structured data, especially when dealing with more complex relations, are scarce in this domain. In this paper, we introduce a benchmark aimed at information extraction of complex entities to support ingredient substitution tasks. Firstly, we present a new dataset - called TASTEset - for fine-grained recognition of food entities in culinary recipes. Secondly, we provide complex entity annotations for substitution on top of the fine-grained entity mentions, which we carefully prepared. We share the dataset and the tasks to encourage progress on more in-depth and complex information extraction from recipes. © 2023 ACM.

关键词： Information retrieval

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Gender-Aware Speech Emotion recognition in Multiple Languages 12th

Gender-Aware Speech Emotion Recognition in Multiple Language...

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12th international conference on pattern recognition Applications and Methods (ICPRAM)

作者： Nicolini, Marco Ntalampiras, Stavros Univ Milan Dept Comp Sci Milan Italy

ISBN: (纸本)9783031547256;9783031547263

this article presents a solution for Speech Emotion recognition (SER) in multilingual setting using a hierarchical approach. the approach involves two levels, the first level identifies the gender of the speaker, while the second level predicts their emotional state. We evaluate the performance of three classifiers of increasing complexity: k-NN, transfer learning based on YAMNet, and Bidirectional Long Short-Term Memory neural networks. the models were trained, validated, and tested on a dataset that includes the big-six emotions and was collected from well-known SER datasets representing six different languages. Our results indicate that there are differences in classification accuracy when considering all data versus only female or male data, across all classifiers. Interestingly, prior knowledge of the speaker's gender can improve the overall classification performance.

关键词： Audio pattern recognition Machine learning Transfer learning Convolutional neural network YAMNet Multilingual speech emotion recognition

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