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检索条件"任意字段=International Conference of Soft Computing and Pattern Recognition"

共 7533 条记录，以下是51-60 订阅

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Visualizing NASDAQ Composite Index Historical Data Using Tableau 6th

Visualizing NASDAQ Composite Index Historical Data Using Tab...

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

作者： Safuan, Halawati Abd Jalil Nyein, Chit Ko Ko Riskhan, Basheer Ashfaq, Farzeen School of Computing and Informatics Albukhary International University Alor Setar Malaysia School of Computer Science SCS Taylors University Subang Jaya Malaysia

ISBN: (纸本)9789819780891

The study explores the visualization and analysis of historical data for the NASDAQ Composite Index (^IXIC) using Tableau. It examines trends in daily closing prices and volume distribution and compares performance with the NYSE index. Findings reveal significant fluctuations in the NASDAQ index over 22 years, suggesting potential future price changes. Forecasted graphs offer insights into potential economic decline and recovery, highlighting the dynamic nature of the market. Volume distribution analysis identifies periods of heightened trading activity. Comparative performance analysis emphasizes different trends observed in the NASDAQ and NYSE indices. Due to market unpredictability and external influences, additional research is crucial for validation. This study showcases the value of data visualization and analysis in finance through Tableau. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Inflation

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Oracle Bone Script recognition Based on Multi-scale Feature Fusion and Knowledge Distillation 27th

Oracle Bone Script Recognition Based on Multi-scale Feature ...

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27th international conference on pattern recognition, ICPR 2024

作者： Wang, Jiaoyan Xu, Xuebin Aysa, Alimjan Ding, Jun Ubul, Kurban School of Computer Science and Technology Xinjiang University Ürümqi830046 China Xinjiang Multilingual Information Technology Key Laboratory Xinjiang University Ürümqi830046 China Joint International Research Laboratory of Silk Road Multilingual Cognitive Computing Xinjiang University Ürümqi830046 China

ISBN: (纸本)9783031783111

Oracle bone scripts, being the oldest and most developed writing system discovered in China till date, have been meticulously studied by numerous scholars. This paper presents the Multi–scale Feature Fusion Attention Net (MFFA–Net) as a solution to the issue of variant characters in the oracle bone affecting recognition accuracy. The network is based on ResNet18 and employs asymmetric convolution alongside a refined coordinate attention technique to acquire image features. It also integrates perceptual field information of varying sizes through hierarchical bilinear pooling. Furthermore, knowledge from the Wide_ResNet101 and DenseNet169 is transferred to MFFA-Net using knowledge distillation techniques. Finally, to validate the effectiveness of our proposed method, we conducted rigorous experiments on the OBC306, OBC265, and EOBC datasets, comparing our results with those of existing methods. The experimental results demonstrate that our method obtains remarkable performance in oracle character recognition, reaching state-of-the-art Top–1 accuracy with 92.42%, 94.78%, and 98.82% on the OBC306, OBC265, and EOBC datasets, respectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Attention Feature Fusion Knowledge Distillation Oracle recognition

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Signal Processing and soft computing Techniques Driven Analysis of Chhattisgarhi Dialects Using MFCCs 1

Signal Processing and Soft Computing Techniques Driven Analy...

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1st IEEE international conference on Smart and Sustainable Developments in Electrical Engineering, SSDEE 2025

作者： Gupta, Madhuri Mishra, Megha Bagga, Jaspal Department of Computer Science Bhilai India Shri Shankaracharya Technical Campus Department of Computer Science Bhilai India Shri Shankaracharya Technical Campus Department of Information Technology Bhilai India

ISBN: (纸本)9798331542108

Chhattisgarhi, spoken by over 12 million people in Chhattisgarh, is the most prominent, holding official language status and serving as a cultural and linguistic identifier. Baheliya and Chamari, though less documented, are vital to the region's linguistic diversity. This research investigates the acoustic and phonetic characteristics of three central Indian dialects - Chhattisgarhi, Baheliya, and Chamari - using advanced machine learning techniques and robust feature extraction methods. Leveraging Mel-Frequency Cepstral Coefficients (MFCCs) for feature extraction, this study examines statistical parameters such as mean, median, and variance to distinguish the spectral properties of these dialects. Our contribution includes a detailed analysis of dialectal variations, revealing that Chamari exhibits the highest mean MFCC1 value (0.223), indicative of its higher spectral energy, while Chhattisgarhi shows the most negative mean MFCC2 (-0.322), reflecting unique spectral distinctions. Variance analysis highlights Baheliya and Chhattisgarhi as having greater spectral diversity, particularly in MFCC1 and MFCC2, showcasing their phonetic variability. These findings underline the acoustic uniqueness of each dialect, bridging gaps in existing research on underrepresented dialects. This work contributes to the preservation of linguistic diversity and provides a strong foundation for designing ASR systems tailored to regional dialects, addressing critical challenges in multilingual speech recognition. © 2025 IEEE.

关键词： Speech recognition

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Face Identification with Feature Learning Using Quasilinear Partial Differential Equation

Face Identification with Feature Learning Using Quasilinear ...

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15th international conference on soft computing and pattern recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired computing, NaBIC 2023

作者： Muthukumaran, V. Satheesh Kumar, S. Radhika, E.K. Arpana, G.K. Nalini, R. Department of Mathematics College of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Computer Science School of Applied Sciences REVA University Bangalore India Department of Computer Science Sindhi College Bangalore India Department of ECE East West College of Engineering Bangalore India

ISBN: (纸本)9783031810855

This study suggests a novel soft computing approach for enhancing face recognition performance using the feature learning methodology. The suggested system of Quasi-linear Partial Differential Equations (QPDE) based face recognition model is used to increase the accuracy of facial identification. The webcam first catches the user's image and saves it to the user's computer. The inference model receives the captured image. The input image is pre-processed to enhance the image, and then the image is split into numerous integral images at the inference model. Using speed-up robust features, an intriguing point is constructed for each integral image. The computed interest point is converted into a QPDE based weight matrix by representing the strength of features at all the regions of the face. With the generated weighted matrix of features extracted from the facial image, the method generates a Fast Artificial intelligence (AI) network with many layers. Each layer of the neural network has many neurons with dedicated functions to compute the similarity and feature weight using QPDE. The proposed approach has produced efficient results compared to earlier methods for the Flickr-Faces-HQ database (FFHQ) datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： soft computing

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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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Video-Based Semi-automatic Drivable Area Segmentation 27th

Video-Based Semi-automatic Drivable Area Segmentation

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27th international conference on pattern recognition, ICPR 2024

作者： Cheng, Zhengyun Zhang, Guanwen Wang, Changhao Zhou, Wei Northwestern Polytechnical University Xi’an China

ISBN: (纸本)9783031784460

Drivable area segmentation is a crucial task in autonomous driving. While current works mainly focus on analyzing single image and overlook the intra-video associations, due to the limited availability of video-based datasets. We present a novel prototype-based approach, named DASeg, to tackle the challenge of annotating intra-video query images using annotated support images as guidance. The primary obstacle lies in aggregating representative prototypes while ensuring resilience to variations in appearance and position across the video. Our method consists of three key components: position embedding for utilizing positional priors, soft-pooling for alleviating the limited coverage of intra-class variations from the support provided, and prototype regularization for generalizability enhancement. We augmented the lane detection dataset VIL-100 by incorporating drivable area annotations, resulting in a new dataset named VDA-100, which was employed to evaluate the performance of the proposed method. Experiments show that our method achieves mIoU score of 88.3% with the pre-trained backbone from lane detection model, and 89.1% when trained from scratch. Our code and dataset is available at https://***/CZY-Code/DASeg. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Drivable area segmentation Positional prior Prototype regularization soft-pooling

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Oracle Character recognition Based on Attention Enhancement and Multi-level Feature Fusion 27th

Oracle Character Recognition Based on Attention Enhancement...

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27th international conference on pattern recognition, ICPR 2024

作者： Han, Zhiwang Yadikar, Nurbiya Xu, Xuebin Aysa, Alimjan Ubul, Kurban School of Computer Science and Technology Xinjiang University Urumqi830046 China Xinjiang Multilingual Information Technology Key Laboratory Xinjiang University Urumqi830046 China Joint International Research Laboratory of Silk Road Multilingual Cognitive Computing Xinjiang University Urumqi830046 China

ISBN: (纸本)9783031781186

Oracle bone characters represent the earliest inscriptions in China. Recognizing and deciphering these characters is significant. Despite some progress made by recent methods, their recognition accuracy remains limited by two major issues: 1) how to focus on characters features within complex background noise images, and 2) how to effectively fuse shallow detail information with deep semantic information. To address these issues, we propose a novel deep learning model called Character Feature Enhancement Network (CFE-Net). The model consists of two key components: Character Feature Enhancement (CFE) and Adaptive Multi-level Classifier Fusion (AMCF). Specifically, CFE utilizes the Spatial Focus Attention Module (SFAM) to focus on extracting foreground character features and suppressing background noise, thereby significantly enhancing high-level semantic representation capabilities. AMCF, on the other hand, achieves multi-level feature fusion by adaptively fusing the outputs of different classifiers, effectively avoiding information loss or interference that simple fusion strategies might cause. We evaluated the CFE-Net on two rubbing oracle bone characters benchmark datasets, OBC306 and Oracle-MNIST. The experimental results demonstrate that CFE-Net significantly outperforms several existing methods in terms of Top-1 accuracy, establishing it as the new state-of-the-art. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantics

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19th international conference on Hybrid Artificial Intelligence Systems, HAIS 2024

19th International Conference on Hybrid Artificial Intellige...

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19th international conference on Hybrid Artificial Intelligence Systems, HAIS 2024

ISBN: (纸本)9783031741821

The proceedings contain 52 papers. The special focus in this conference is on Hybrid Artificial Intelligence Systems. The topics include: The Third Codon Nucleotide’s Role in Genetic Recombination Within SARS-CoV-2 Spike Protein: A Pilot Study;machine Learning for the Identification of Biomarker and Risk Factors associated with Depression in Adult Population: Preliminary Results on a Small Cohort;Unveiling HIV-1 U Sequences: Shedding Light Through Transfer Learning on Genomic Spectrograms;model to Early Detection of Autism Spectrum Disorder Through Opinion Mining Approach;A Short Analysis of Hybrid Approaches in COVID‑19 for Detection and Diagnosing;A Graph Neural Network with Multi-head Attention for Universal Brain Disease Diagnosis from fMRI Images;a Computer Vision Approach to Detect Facial Characteristics Related to Encephalopathy in Term Infants;SPADE Norms: a Distributed General Framework for Normative Multi-agent Systems;Finding Optimal Classroom Arrangements to Minimize Cheating in Exams Using a Hybrid AI System;resilience to the Flowing Unknown: An Open Set recognition Framework for Data Streams;a Comparison Procedure for the Evaluation of Metaheuristics;Nyström and RFF Ensembles for Large-Scale Kernel Predictions;application of Transfer Learning to Online Models in Malware Detection;a New Training Algorithm for Support Vector Machines;soft Adaptive Segments for Bio-Inspired Temporal Memory;assessing Generative Artificial Intelligence in Fundamental Physics with Gaussian Processes;implementation of Classical Decision Trees in a Quantum computing Paradigm;machine Learning Methods as Robust Quantum Noise Estimators;The Impact of Data Annotations on the Performance of Object Detection Models in Icon Detection for GUI Images;bayesian Model Selection Pruning in Predictive Maintenance;neonates Crying Detection Through Feature Extraction and Machine Learning Methods;differentiable Prototypes with Distributed Memory Network for Continual Learning;efficient Continu

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19th international conference on Hybrid Artificial Intelligence Systems, HAIS 2024

19th International Conference on Hybrid Artificial Intellige...

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19th international conference on Hybrid Artificial Intelligence Systems, HAIS 2024

ISBN: (纸本)9783031741852

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Student Performance Prediction Analysis Using Neural Networks 15th

Student Performance Prediction Analysis Using Neural Network...

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15th international conference on soft computing and pattern recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired computing, NaBIC 2023

作者： Prashanth, Mallellu Sai Uma Maheswari, V. Aluvalu, Rajinikanth Prasad Kantipudi, M.V.V. Symbiosis Institute of Technology Hyderabad Campus Symbiosis International University Pune India Department of Computer Science and Engineering Chaitanya Bharathi Institute of Technology Hyderabad India Symbiosis Institute of Technology Pune Campus Symbiosis International University Pune India

ISBN: (纸本)9783031810824

This research paper presents a comprehensive analysis of student performance based on a merged dataset from two subjects: Mathematics and Portuguese. The dataset encompasses a wide range of student attributes and behaviours, allowing us to explore various factors influencing academic success. The study employs exploratory data analysis, data visualization, statistical tests, and machine learning techniques to uncover patterns and relationships within the data. The analysis begins with data preprocessing, including column renaming, categorical variable creation, and merging of datasets. Visualizations such as count plots, heatmaps, and distribution plots are utilized to understand the distribution of final grades and correlations between attributes. Furthermore, the study investigates the impact of romantic status, weekend alcohol consumption, parental education, social activities, desire for higher education, and urban/rural living areas on student performance. Chi-square tests for association are conducted to assess the significance of relationships between categorical variables and final grades. Machine learning models, including Decision Trees, Random Forests, Support Vector Machines, Logistic Regression, AdaBoost, and Stochastic Gradient Descent, are employed to predict student performance. Feature selection techniques are also employed to determine the optimal number of features for achieving high prediction accuracy. Through this comprehensive analysis, we gain insights into the complex interplay of various factors affecting student performance. The findings contribute to a deeper understanding of academic success and highlight potential areas for intervention and improvement. This research serves as a valuable resource for educators, policymakers, and researchers seeking to enhance educational outcomes and facilitate targeted interventions for students. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Decision Tree Neural Networks Prediction Visualization

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