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Customized hidden layered ann based pattern recognition technique for differential protection of power transformer 11th

Customized hidden layered ann based pattern recognition tech...

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11th international conference on soft computing and pattern recognition, SoCPaR 2019, and 11th World Congress on Nature and Biologically Inspired computing, NaBIC 2019

作者： Balaga, Harish Marrapu, Deepthi Vardhaman College of Engineering Hyderabad India

ISBN: (纸本)9783030493448

This article presents the use of Customized Multi-Layer ANN based pattern recognition Technique for the numerical differential protection of a power transformer. An efficient Resilient Back Propagation trained neural network model with customized parallel hidden layers is proposed for the said purpose. The task of the ANN is to discriminate among various operating conditions of the transformer and issue trip signal, only in the case of internal fault. The data base required for the training of algorithm is obtained by using MATLAB/SIMULINK environment. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Power transformers

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Automated Resume Parsing: A Natural Language Processing Approach 7

Automated Resume Parsing: A Natural Language Processing Appr...

引用

7th IEEE international conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023

作者： Sougandh, Thatavarthi Giri Sai Snehith, K. Reddy, Nithish Sagar Belwal, Meena Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350343144

The extraction of critical information from resumes, such as contact information, skills, education, and job experience, requires the use of resume parsing. In this work, we propose a resume parser that integrates two methodologies: a Named Entity recognition (NER) model approach and a Keyword and pattern Matching model approach using Regular Expressions (Regex), utilizing certain NLP libraries and methods. The NER model makes use of NLP libraries to precisely recognize and categorize named entities-such as names, phone numbers, and email addresses-in the resume text. In order to provide a thorough profile overview, it also organizes the parts on talents, education, and job experience. In addition, the Keyword and pattern Matching model makes use of Regex and pre-established rules to extract certain information, such job titles, firm names, and years of experience. Our resume parser uses NLP-based approaches to increase accuracy and performance, allowing it to handle various resume formats and deliver trustworthy results. Performance assessments show how well the parser extracts crucial information, even from resumes with various layouts and formats. Our resume parser seems to be a useful tool for processing huge numbers of resumes in real-world applications due to its use of NLP libraries and methodologies, together with excellent accuracy and processing speed. © 2023 IEEE.

关键词： pattern matching

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Advances in Facial Emotion recognition: Deep Learning Approaches and Future Prospects

Advances in Facial Emotion Recognition: Deep Learning Approa...

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MIT Art, Design and Technology School of computing international conference (MITADTSoCiCon)

作者： Shrikala Deshmukh Snehal Chaudhary Milind Gayakwad Kalyani Kadam Nitin S. More Amol Bhosale Amity University Mumbai India Department of Information Technology College of Engineering Bharati Vidyapeeth (Deemed to be University) Pune India College of Engineering Bharati Vidyapeeth (Deemed to be University) Pune India Symbiosis Institute of Technology Pune Symbiosis International (Deemed University) Pune India Department of CSE School of Computing MIT Art Design and Technology University Pune

ISBN: (数字)9798350362879

ISBN: (纸本)9798350362886

Facial Emotion recognition (FER) has evolved significantly with the advent of deep learning techniques, offering novel approaches to interpret and process facial expressions. This paper reviews recent advancements in FER, emphasizing the role of deep neural network architectures and their application across diverse fields including security, health, and human-machine interfaces. We analyze various methodologies, technological advancements, challenges, and future trends in FER, providing insights into the current state-of-the-art. We conclude by discussing the limitations of current FER systems and propose future directions for research, particularly in extending emotion recognition beyond basic emotions and incorporating multimodal approaches.

关键词： Deep learning Training Emotion recognition Accuracy Reviews Face recognition soft sensors

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A Multimodal Approach for Emotion recognition in Conversations Using the MELD Dataset

A Multimodal Approach for Emotion Recognition in Conversatio...

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Cybersecurity, Internet of Things and soft computing (CITSC), Asia-Europe conference on

作者： Jiarong He Dundee International Institution Central South University Changsha China

ISBN: (数字)9798331504205

ISBN: (纸本)9798331504212

Emotion recognition in conversational contexts is a fundamental task in affective computing, with significant implications for applications in empathetic dialogue systems, social robotics, and advanced media analysis. Given the complexity of emotional expression, effective emotion recognition systems must integrate multiple modalities, including linguistic, acoustic, and visual cues. This paper presents a multimodal framework for emotion recognition, evaluated on the Multimodal EmotionLines Dataset (MELD) dataset, which consists of multiparty dialogues containing textual, acoustic, and visual data. The proposed system leverages a pretrained Bidirectional Encoder Representations from Transformers (BERT) model for text encoding, Mel-frequency cepstral coefficients (MFCCs) for acoustic features, and Residual Network-50 (ResNet-50) for extracting visual embeddings. These modality-specific features are then fused through a concatenation mechanism and passed through a linear classifier to predict one of seven emotion categories: neutral, joy, sadness, anger, surprise, fear, and disgust. Experimental results demonstrate that the proposed approach achieves a test accuracy of 73.56%, a weighted F1-score of 0.7254, outperforming unimodal baselines. Analysis of the confusion matrix indicates that the model excels at classifying common emotions, such as neutral and joy, but struggles with more subtle emotions, such as fear and disgust, which are often misclassified. Further evaluations, including ablation studies and comparisons with existing methods, underscore the efficacy of multimodal fusion. Future research will explore the integration of temporal attention mechanisms, context modeling across dialogue turns, and improved feature extraction strategies for more precise emotion recognition.

关键词： Emotion recognition Visualization Analytical models Affective computing Accuracy Feature extraction Transformers Encoding Robots Context modeling

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Enhanced Discriminant Local Direction pattern Learning for Robust Palmprint Identification 22nd

Enhanced Discriminant Local Direction Pattern Learning for R...

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22nd international conference on Parallel and Distributed computing, Applications and Technologies (PDCAT 2021)

作者： Ma, Siyuan Hu, Qintai Zhao, Shuping Jiang, Lin Wu, Wenyan Guangdong Univ Technol Guangzhou Peoples R China

ISBN: (纸本)9783030967727;9783030967710

Direction-based methods have been widely used in palmprint recognition methods. However, most existing palmprint direction patterns-based methods need rich prior knowledge, and usually ignores the relationships among different samples. Furthermore, how to make the extracted features more discriminative is also a dilemma to improving the recognition performance. To solve these problems, we propose to learn enhanced discriminative direction pattern in this study. We first extract the complete and stable local direction patterns, where a salient convolution average feature (EDL) is extracted from the palmprint image. Afterwards, a linear regression learning model is introduced to enhance the discriminant of EDL, such that the representation of the direction pattern can be improved. Experimental results on 4 real-world palmprint databases show that the proposed method can outperform the other state-of-the-art related methods.

关键词： Robust palmprint identification Complete and stable local direction feature Discriminative projection learning

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Improvement of soft-YOLOv5 Algorithm for Stray Cats and Dogs Detection

Improvement of Soft-YOLOv5 Algorithm for Stray Cats and Dogs...

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pattern recognition and Artificial Intelligence (PRAI), international conference on

作者： Zhen Zhang Shutong Li Dan Zhang Jingyi Chen Xiling Chi Zhengrui Zhang School of Computer Science and Engineering Huizhou University Huizhou China

Real-time monitoring and instantaneous tracking of stray cats and dogs within a specific area using image recognition and object detection in computer vision technology is currently the predominant trend. Cat and dog recognition, as a classic object detection project, serves the purpose of identifying the quantity and categories of cats and dogs in a single image or video frame. This study builds upon the YOLOv5 framework and addresses the practical challenges of cat and dog detection, such as overlapping and occlusion. To enhance detection accuracy and stability, the soft-NMS and Merge-NMS algorithms are contrasted with YOLOv5's NMS algorithm. The improved soft-NMS and Merge-NMS algorithms are evaluated through performance testing and compared with the original NMS algorithm. As a result of these two algorithm enhancements for YOLOv5's NMS algorithm, the soft-NMS algorithm demonstrates a 2.2% performance improvement over YOLOv5 using the NMS algorithm. Therefore, soft-YOLOv5 is better suited for the task of detecting stray cats and dogs in urban communities and streets.

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soft Label and Discriminant Embedding Estimation for Semi-Supervised Classification 25

Soft Label and Discriminant Embedding Estimation for Semi-Su...

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25th international conference on pattern recognition (ICPR)

作者： Dornaika, F. Baradaaji, A. El Traboulsi, Y. Univ Basque Country UPV EHU San Sebastian Spain Basque Fdn Sci Ikerbasque Bilbao Spain

ISBN: (纸本)9781728188089

In recent times, graph-based semi-supervised learning proved to be a powerful paradigm for processing and mining large datasets. The main advantage relies on the fact that these methods can be useful in propagating a small set of known labels to a large set of unlabeled data. The scarcity of labeled data may affect the performance of the semi-learning. This paper introduces a new semi-supervised framework for simultaneous linear feature extraction and label propagation. The proposed method simultaneously estimates a discriminant transformation and the unknown label by exploiting both labeled and unlabeled data. In addition, the unknowns of the learning model are estimated by integrating two types of graph-based smoothness constraints. The resulting semi-supervised model is expected to learn more discriminative information. Experiments are conducted on six public image datasets. These experimental results show that the performance of the proposed method can be better than that of many state-of-the-art graph-based semi-supervised algorithms.

关键词： Graph-based embedding discriminant embedding soft labels semi-supervised learning pattern recognition image categorization

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Virtual Prototype Design of Flexible Exoskeleton based on pattern recognition Technology 4

Virtual Prototype Design of Flexible Exoskeleton based on Pa...

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4th IEEE international conference on Power, Intelligent computing and Systems, ICPICS 2022

作者： Chen, Xin Li, Dong Han, Chang Cheng, Peng Chen, Yi Wei, Lingling Wuhan Business University Wuhan China Wuhan Vocational College of Technology Wuhan China

ISBN: (数字)9781665467735

ISBN: (纸本)9781665467735

the purpose of the research on flexible exoskeleton robot is to develop intelligent equipment suitable for human body to wear. This kind of exoskeleton can quickly provide additional energy compensation for human joints, so as to enhance joint motion function. The design of lower limb flexible exoskeleton should be combined with the characteristics of convenience of wearing, stability of use and rapidity of response. In this paper, according to the skeleton and muscle structure of human lower limbs under healthy condition, a kind of flexible exoskeleton which can be quickly worn in complex environment is designed. The exoskeleton is integrated into the shoes, and its main assistance points are ankle joint and knee joint, which greatly improves the wearing efficiency. After the design of flexible exoskeleton is completed, the simulation analysis of exoskeleton is carried out to verify that the design meets the use requirements. © 2022 IEEE.

关键词： pattern recognition

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Food Image Classification Based on Residual Network 19th

Food Image Classification Based on Residual Network

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19th international conference on Advanced Intelligent computing Technology and Applications (ICIC)

作者： Yang, Xueyan Sun, Jinping Wang, Zhuo Bao, Wenzheng Xuzhou Univ Technol Sch Informat Engn Xuzhou 221018 Jiangsu Peoples R China

ISBN: (纸本)9789819947546;9789819947553

As food culture and internet technology evolve, tracking the nutritional information of daily food intake becomes increasingly important for assessing dietary habits and health management status. However, effective food image classification is a prerequisite. Food images present a fine-grained image recognition problem characterized by large inter-class differences and small intra-class differences. The presence of mutual occlusion between foods and background noise challenges existing food image classification techniques in extracting robust visual features. In response to these challenges, this paper proposes a food image classification residual network incorporating pyramid segmentation attention and soft thresholding. Attention is applied across both spatial and channel dimensions to mitigate the impact of noisy data on classification results. In each improved residual block, pyramid segmentation attention (PSA) is embedded to replace the convolutional unit, extracting target features through the spatial-level visual attention vector and multi-scale response map. Concurrently, a soft thresholding sub-network is embedded within the basic module of the network, employing channel attention to automatically learn the threshold for each sample, thereby suppressing redundant information in the image. Multiple experiments were conducted using the VireoFood-251 food dataset, with results indicating a classification accuracy of 87.03%. When compared to classical models ResNet34 and ResNet50, the accuracy increased by 5.71% and 3.02%, respectively, validating the feasibility of the proposed network framework.

关键词： Classification Pyramid split attention soft thresholding Residual block

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Synergistic Solutions for Cloud Cybersecurity and Financial Operations using AI, Blockchain and Quantum computing

Synergistic Solutions for Cloud Cybersecurity and Financial ...

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Cybernation and Computation (CYBERCOM), international conference on

作者： Sunil Boopalan Department of Computer Applications SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798331542344

ISBN: (纸本)9798331542351

This research paper further builds on previous research by proposing a combination of AI and blockchain in the context of integration of quantum computing to improve the cybersecurity of cloud and financial operations, especially for India's UPI and digital rupee platforms. This is especially the case when digital transactions are on the rise and their security against rampant cyber threats and overall soundness of operations must be guaranteed. The following is a novel framework that embellishes AI, blockchain, and quantum computing to meet these challenges outlined in this paper. Machine learning and pattern recognition will be implemented to scope out and prevent security threats, while blockchain is to ensure a secure and open system of transactions; quantum computing will help to provide extraordinary features for data security and data encryption. The research also seeks to examine the current implementation approaches, advantages, and disbursement issues of this integration and subsequently makes recommendations that can help policymakers and practitioners make improvements for the improvement of trust and security in the implementation of financial solutions on the cloud.

关键词： Technological innovation Quantum computing Stability criteria Open systems Machine learning Blockchains pattern recognition Encryption Standards Guidelines

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