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On-Chip Learning with a 15-neuron Digital Oscillatory Neural Network Implemented on ZYNQ Processor 22

On-Chip Learning with a 15-neuron Digital Oscillatory Neural...

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international conference on Neuromorphic Systems (ICONS)

作者： Abernot, Madeleine Gil, Thierry Todri-Sanial, Aida Univ Montpellier LIRMM CNRS Montpellier France

ISBN: (纸本)9781450397896

Real-time on-chip learning is an important feature for current neuromorphic computing to enable smart embedded systems capable of learning. Neuromorphic computing based on Oscillatory Neural Networks (ONNs) are networks of coupled oscillators computing with phase information. ONNs with fully-connected connections can perform auto-associative memory applications when trained with unsupervised learning rules. In this paper, we propose for the first time an architecture to perform on-chip learning with a digitally implemented ONN. We implement the digital ONN with programmable logic of a ZYNQ processor and we perform learning on the processing system of the same chip. We validate our solution on a 15-neuron ONN trained with either Hebbian or Storkey learning rules up to three patterns. We report a stable resource utilization for both learning rules and timing from 119 mu s (Hebbian) to 163 mu s (Storkey). Additionally, accuracy is equal to the off-chip learning implementation.

关键词： Oscillatory Neural Networks Auto-associative Memory On-chip Learning pattern recognition

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CNN-based Model for Gender and Age Classification based on Palm Vein Images 12

CNN-based Model for Gender and Age Classification based on P...

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12th international conference on pattern recognition Systems (ICPRS)

作者： Hernandez-Garcia, Ruber Feng, Zheng Barrientos, Ricardo J. Manuel Castro, Francisco Ramos-Cozar, Julian Guil, Nicolas Univ Catolica Maule CIEAM Res Ctr Talca Chile Univ Sydney Sch Elect & Informat Engn Sydney Australia Univ Catolica Maule Fac Engn Sci Talca Chile Univ Malaga Dept Comp Architecture Malaga Spain

ISBN: (纸本)9781665466943

Automatically predicting gender and age group from biometrics traits is an essential and challenging task in many real-world applications. There are several works about using machine learning methods to identify human gender or age through the face, iris, or fingerprint, but only limited research about using palm vein patterns. Considering the powerful feature representation ability of Convolutional Neural Networks (CNN) and the advantages of palm vein biometrics, this paper introduces a new CNN-based method for gender and age classification based on palm vein images. Experimental results show that the proposed model is able to learn discriminative features from palm vein images for these tasks, achieving state-of-the-art results on the VERA database by using a shallow CNN architecture. Besides, the obtained results suggest the feasibility of further studies on multi-task identification approaches and the reduction of the penetration rate in massive databases.

关键词： Palm vein images Gender classification Age estimation Convolutional neural networks soft biometrics

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2nd international conference on Emerging Trends in Expert Applications and Security, ICETEAS 2023

2nd International Conference on Emerging Trends in Expert Ap...

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2nd international conference on Emerging Trends in Expert Applications and Security, ICETEAS 2023

ISBN: (纸本)9789819919086

The proceedings contain 48 papers. The special focus in this conference is on Emerging Trends in Expert Applications and Security. The topics include: Securing Account Hijacking Security Threats in Cloud Environment Using Artificial Neural Networks;ioT-Based Smart Pill Box and Voice Alert System;early Identification of Plant Diseases by Image Processing Using Integrated Development Environment;usability Attributes and Their Mapping in Various Phases of software Development Life Cycle;controlling Devices from Anywhere Using IoT Including Voice Commands;ioT-Based Air Quality Monitoring System;modeling of Order Quantity Prediction using soft computing Technique: A Fuzzy Logic Approach;development of Classification Framework Using Machine Learning and pattern recognition System;Human Part Semantic Segmentation Using Custom-CDGNet Network;current Web Development Technologies: A Comparative Review;effect on Compressive Strength of Portland Pozzolana Cement on Adding Admixtures Using Machine Learning Technique;tech Track for Visually Impaired People;intelligent Compression of Data on Cloud Storage;diagnosis of Diabetic Retinopathy Using Deep Neural Network;multi-parameter Sensor-Based Automation Farming;comparing Ensemble Techniques for Bilingual Multiclass Classification of Online Reviews;detection of Disease in Liver Image Using Deep Learning Technique;how to Quantify software Quality Factors for Mobile Applications?: Proposed Criteria;dysgraphia Detection Using Machine Learning-Based Techniques: A Survey;Designing AI for Investment Banking Risk Management a Review, Evaluation and Strategy;R-Peak-Based Arrhythmia Detection as an Impact of COVID-19;a Neutrosophic Cognitive Maps Approach for Pestle Analysis in Food Industry;assistive Agricultural Technology—Soil Health and Suitable Crop Prediction;smart Chatbot for Guidance About Children’s Legal Rights.

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Using LUPI to Improve Complex Activity recognition 1

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3rd international conference on Activity and Behavior computing (ABC)

作者： Adachi, Kohei Lago, Paula Hattori, Yuichi Inoue, Sozo Kyushu Inst Technol Kitakyushu Fukuoka Japan Univ Nacl Abierta & Distancia Bogota Colombia

ISBN: (数字)9789811903618

ISBN: (纸本)9789811903618;9789811903601

Sensor-based activity recognition can recognize simple activities such as walking and running with high accuracy, but it is difficult to recognize complex activities such as nursing care activities and cooking activities. One solution is to use multiple sensors, which is unrealistic in real life. Recently, learning using privileged information (LUPI) has been proposed, which enables training using additional information only in the training phase. In this paper, we used LUPI for improving the accuracy of complex activity recognition. In short, training is performed with multiple sensors during the training phase, and a single sensor is used during testing. We used four published datasets for evaluating our proposed method. As a result, our proposed method improves by up to 16% in F1-Score to 67% compared to the baseline method when we used random-split cross-validation of each subject.

关键词： pattern recognition

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Enhancing Foundation Model Robustness for Multi-center Real-World Medical Image Analysis 1st

Enhancing Foundation Model Robustness for Multi-center Real...

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1st MICCAI Challenge Comprehensive Analysis and computing of Real-World Medical Images, CARE 2024 Held in Conjunction with 27th international conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2024

作者： Huang, Jinlong Sun, Xiao Wang, Lisheng Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9783031870088

Revolutionizing medical image analysis, the Segment Anything Model (SAM) stands out among emerging foundation models, yet its potential in accurately segmenting small, irregular ROIs from multi-center, diverse datasets remains untapped in real-world scenarios. Our study harnesses SAM’s capabilities to address the segmentation intricacies of clinically crucial, yet challenging, features in complex medical imaging. To bridge this gap, we embark on a comprehensive exploration anchored in Foundation models including SegVol and MedSAM, targeting three pivotal challenges: 1) enhancing the generalization of models across diverse medical image modalities and sequences, 2) reinforcing the stability of interactive segmentation, and 3) overcoming the segmentation hurdles posed by minuscule, atypical ROIs. We employ a coarse-to-fine segmentation strategy, using the CARE24 dataset alongside a meticulously publicly available dataset to fine-tune these models for enhanced real-world performance. To boost the stability and precision of interactive segmentation, we devise a retest mechanism, ensuring consistent outcomes in user-guided processes. Furthermore, recognizing the potential of multimodal data, we introduce a fusion strategy, capitalizing on complementary information to improve overall segmentation accuracy and adaptability. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Complicated ROIs Foundation model Multi-center Segment Anything Model (SAM)

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9th international conference on Frontiers in Intelligent computing: Theory and Applications, FICTA 2021

作者： Ajay, D. Pon Hidaya David, P. Tirupattur635 601 India

ISBN: (纸本)9789811666155

The aim of this paper is to introduce an alternative pattern Recognizing technique using similarity measure in the spherical fuzzy set environment. In many applications of pattern recognition, the structural data is intrinsically ambiguous. It is essential to boost the descriptive power of pattern recognition in such applications, and the fuzzy technique is often used. So, a fuzzy logic (FL)-based similarity measure (SM) called Exponential similarity measure for Spherical fuzzy sets (SFSs) is applied in the field of pattern Recognizing. In this paper similarity measures known as Exponential Similarity Measure (ESM), Weighted Exponential Similarity Measure (W-ESM), Weighted Average Exponential Similarity Measure (WA-ESM), Weighted Generalized Average Exponential Similarity Measure (WGA-ESM) are developed. Spherical fuzzy set is extended from fuzzy sets to cope with uncertain situations more accurately. Further, a problem on Intrusion Detection and Risk evaluation is chosen as an application of pattern recognition and is solved using similarity measures proposed in this paper. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： pattern recognition

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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...

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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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