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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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2025 Asia-Europe conference on Cybersecurity, Internet of Things and soft computing, CITSC 2025

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

ISBN: (纸本)9798331504205

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. © 2025 IEEE.

关键词： Emotion recognition

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Multi Dimensional Deep Encoding for Categorical Feature Space 24

Multi Dimensional Deep Encoding for Categorical Feature Spac...

引用

13th international conference on computing and pattern recognition, ICCPR 2024

作者： Alamuri, Madhavi Surampudi, Bapiraju Negi, Atul School of Computer and Information Sciences University of Hyderabad Telangana State Hyderabad India Cognitive Science Lab International Institute of Information Technology Telangana State Hyderabad India

ISBN: (纸本)9798400717482

Categorical data classification and clustering are essential to many fields, including pattern recognition, data mining, knowledge discovery, and machine learning. It is crucial to understand how to provide categorical input to machine learning algorithms because the majority of them are developed for numerical inputs. Any machine learning algorithm's performance depends not only on the model and hyper parameters but also on the way the data is prepared for and fed into the model. With high cardinality features and categorical predictor variables, machine learning approaches that handle categorical data frequently encounter challenges. In this scenario, we investigate categorical encoding techniques, develop a taxonomy of encoding techniques, and use the machine learning algorithms KNN, MLP, SVM, and ELM and make a comparative study. We suggest a method of hybrid encoding using CNN that gives better performance results. In this paper, we propose the machine learning strategy employing multi-dimensional encodings with CNN, which outperforms other machine learning approaches on majority datasets. It is clear that CNN's performance in learning categorical features has been much enhanced by the use of multi-dimensional encoded input vectors. In our suggested method for supervised and unsupervised encoding, we have used four distinct encodings. These results encourage us to further study the outcomes of different hybrid combinations of encodings for categorical feature classification. © 2024 Copyright held by the owner/author(s).

关键词： Categorical data

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Traffic Sign Detection Using Deep Learning 15th

Traffic Sign Detection Using Deep Learning

引用

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： Galety, Muhammad Gouse Samarkand International University of Technology Samarkand Uzbekistan

ISBN: (纸本)9783031810824

The recognition of traffic signs, also known as TSR, is an essential component of many advanced driver assistance systems (ADAS) and autonomous driving systems (ADSs). Traffic sign detection (TSD), the first critical stage of TSR, is a tough challenge because of the various types of traffic signs, their small proportions, difficult driving conditions, and obstructions. TSD methods based on machine recognition and pattern classification have increased recently. This study's primary goal is to research Traffic Sign Detection using Deep Learning Techniques, and the method used in this research is Deep Learning Models like Faster R-CNN and YOLOv5. Convolutional Neural Networks are used for identifying and classifying road signs because they begin with an input image, assign weights to various elements of that image, and then differentiate those weights' respective aspects from one another. According to the results of this research, YOLOv5 is essential, even though its accuracy rate is slightly lower than Faster RCNN. In actual TSR, a faster recognition speed is frequently needed. TSR should use YOLOv5. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Computer Vision Object Detection Traffic Sign Detection (TSD) Traffic Sign recognition (TSR)

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Complex scene fall detection method based on improved YOLOv5 24

Complex scene fall detection method based on improved YOLOv5

引用

13th international conference on computing and pattern recognition, ICCPR 2024

作者： Li, Yifan Liu, Pengyu Kong, Lili Deng, Yiyang Liu, Weilong Chen, Shanji Chen, Jiali Yuan, Bowen School of Information Science and Technology Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing China Hebei Hongyi Environmental Protection Technology Co. Ltd Dingzhou Hebei China Beijing-Dublin International College at BJUT Beijing University of Technology Beijing China Information Technology Co. Ltd Dingzhou Hebei China College of Physics and Electronic Information Engineering Qinghai Minzu University Qinghai China

ISBN: (纸本)9798400717482

Aiming at the existing deep learning-based fall detection model in multiple complex scenes such as multi-person scenes, long-distance scenes, and occlusion scenes, there are problems such as leakage detection and false detection. In this paper, we take the falling person as the research object, construct a dataset containing multiple complex scenes such as multi-person scenes, long-distance scenes, and occlusion scenes, and propose a fall detection model based on improved YOLOv5. By improving the backbone network and neck network structure of the detection model, the problem of low accuracy of fall detection due to incomplete target deformation features and target detail features in complex scenes is solved. The proposed algorithm in this paper achieves an average accuracy of 92.78% for fall detection in complex scenes, and the detection speed reaches 36 frames/second. © 2024 Copyright held by the owner/author(s).

关键词： Fall detection

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Detection of Islanding in AC Microgrid Using Random Forest And Scalogram Image Based Local Binary pattern 4

Detection of Islanding in AC Microgrid Using Random Forest A...

引用

4th international conference on Power, Control and computing Technologies, ICPC2T 2025

作者： Pal, Kumaresh Namrata, Kumari Akella, Ashok Kumar Meena, Veerpratap Padmanaban, Sanjeevikumar Department of Electrical Engineering National Institute of Technology Jamshedpur India Department of Electrical Engineering IT and Cybernetics University of South-Eastern Norway Porsgrunn Norway

ISBN: (纸本)9798331530402

Islanding is a significant obstacle along with excessive penetration of DG sources. The islanding could cause harm to the customers and their equipment. The IEEE 1547 DG interconnection regulations stipulate that the DG must be disconnected, and the islanding must be observed within two seconds. Basically, this paper presents a novel method for detecting islanding based upon image pattern recognition using random forest (RF) classification technique is proposed. Images are utilized to derive the local binary pattern (LBP) characteristics to identify non-islanding as well as islanding events. Hence, the time series signal is obtained and transformed into scalogram images at the point of common connection. These images are utilized to extract the features using LBP, which is then utilized as a feature vector input for the RF classifier for training as well as testing. It uses the total harmonics distortion (THD) characteristic of the voltage to derive scalogram image. The RF classifier's effectiveness is assessed with 5-fold cross-validations. The result from classification demonstrates that detection of islanding using RF classifier and LBP feature being used for image pattern recognition achieved excellent results with 98.89% accuracy and detection time of 158 milliseconds. © 2025 IEEE.

关键词： Local binary pattern

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Design and Implementation of a Wireless Monitoring Application for Pregnant Women's Fetal Movement

Design and Implementation of a Wireless Monitoring Applicati...

引用

2025 Asia-Europe conference on Cybersecurity, Internet of Things and soft computing, CITSC 2025

作者： Qin, Manping Xu, Yong Chan, Choon Kit Chen, Zhiling Zhu, Caitong School of Artificial Intelligence and Smart Manufacturing Hechi University Hechi China Inti International University Faculty of Engineering and Quantity Surveying Negeri Sembilan Nilai Malaysia

ISBN: (纸本)9798331504205

The fetal movement signal represents the most intuitive indicator of the health of the fetus and the mother. In order to effectively monitor the health status of the fetus and the pregnant woman in the third trimester, an application was designed based on the Android operating system for real-time monitoring of fetal movement in pregnant women. Using Android Studio as the development platform, the SQLite database was utilized to facilitate user registration and login. The low-power Bluetooth API is utilized to establish the connection between the mobile terminal and the wireless fetal movement monitor for pregnant women, enabling the collection of raw fetal movement data. Real-time transmission of data to the smartphone via low-power Bluetooth is achieved, followed by the processing and recognition of fetal movement signals on the app, culminating in the visualization of the recognition results. Through rigorous testing and application, the system facilitates the visualization of fetal health data, addressing family concerns about the health of pregnant women and fetuses, and is applicable in the field of fetal health monitoring. © 2025 IEEE.

关键词： Fetal monitoring

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3rd international conference on Computational Electronics for Wireless Communications, ICCWC 2023

3rd International Conference on Computational Electronics fo...

引用

3rd international conference on Computational Electronics for Wireless Communications, ICCWC 2023

ISBN: (纸本)9789819719457

The proceedings contain 35 papers. The special focus in this conference is on Computational Electronics for Wireless Communications. The topics include: Design of a Four-Port MIMO Antenna for C-, X-, and Ku-Band Applications;Exploration of Diverse Techniques for Design and Optimization of QCA-Based Multiplexer Circuits;melanoma Detection Via Deep Convolutional Neural Network;pattern Diversity mm-Wave Rectangular Ring Shaped MIMO Antenna for 5G Communication and Internet of Things Applications;Flexible Two/Eight-Port MIMO Antenna Configuration for LTE B41/WLAN/Wi-Fi 6E and C-Band Applications;Improvement in Machining Ability of Tungsten Carbide Tool Insert Through Microwave Hybrid Heating During Turning of EN 24 Steel;optimizing Airfare Pricing: A Data-Driven Approach for Affordable Travel Planning;advancements in Emotion recognition: Systematic Review and Research Roadmap;epoc-Based Electroencephalography Signals Analysis of Different Stress Levels;Two-Element Tilted Rectangular MIMO UWB Antenna for Underwater Communications;deep Learning-Based Maize Crop Disease Detection and Remedial Recommendation System;design of Splash Plate Dielectric Hat Feed for Ka-Band Tracking Radar Applications;soft computing Paradigms for Load Balancing in Cloud computing;strategies for Effective Network Congestion Control: Insights from Parameter-Based Analysis;BICC: Optimizing Sensor Network Performance Via an Efficient Bioinspired Iterative Approach with Congestion Control;Leakage Power Reduction and Stability Analysis of 5 nm Node GAA CNTFET SRAMs;bandwidth Enhancement and Improvement of Directivity of a Composite Conical Structure in the Presence of a Circular Loop Antenna;development of Compact Dual Polarized Corrugated Feed Horn Antenna with Low Cross-Polarization for Doppler Weather Radar Applications in C-Band;A Review on Recent Breakthroughs and Accomplishments in the Development of FeFET.

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3rd international conference on Computational Electronics for Wireless Communications, ICCWC 2023

3rd International Conference on Computational Electronics fo...

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3rd international conference on Computational Electronics for Wireless Communications, ICCWC 2023

ISBN: (纸本)9789819719426

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A Cross-Scale Multi-Branch Feature Fusion Based Method for Smoke and Fire Detection in Warehouses 24

A Cross-Scale Multi-Branch Feature Fusion Based Method for S...

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13th international conference on computing and pattern recognition, ICCPR 2024

作者： Bai, Yanbing Liu, Pengyu Liu, Weilong Deng, Yiyang Kong, Lili Chen, Shanji Chen, Jiali Yuan, Bowen School of Information Science and Technology Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University Of Technology Beijing China Information Technology Co. Ltd Dingzhou China Beijing-Dublin International College at BJUT Beijing University Of Technology Beijing China College of Physics and Electronic Information Engineering Qinghai Minzu University Qinghai China

ISBN: (纸本)9798400717482

To address the challenges of leakage, misdetection, and low accuracy caused by environmental complexity, object occlusion, and insufficient lighting in smoke and fire detection within warehouse scenarios, this paper proposes a cross-scale multi-branch feature fusion network for smoke and fire detection, named SF-YOLO. Firstly, a multi-branch feature enhancement module, Res2Net-C, is proposed to introduce an attention mechanism using a multi-branch feature fusion network, which not only extends the sensory field of the network, but also enhances the ability of extracting effective information, while effectively suppressing the interference of irrelevant information. It effectively improves detection performance under complex environmental conditions and in the presence of object occlusion. Secondly, a cross-scale feature fusion network structure, MSFPN, is proposed, which achieves effective complementation and fusion of high-level and low-level information by establishing hopping connections between features at different scales, and significantly improves the recognition accuracy of the type and location of the smoke and fire in low-light conditions. In addition, the added small target detection layer further improves the network's ability to detect small targets. Finally, in order to better adapt to pyrotechnic targets with variable morphology, EIoU is adopted as the bounding box loss function. Test results on a self-built dataset show that SF-YOLO achieves a mean accuracy rate (mAP) of 87.5% in the task of detecting smoke and fire under multiple environmental factors in a warehouse scenario, proving its effectiveness and usefulness. © 2024 Copyright held by the owner/author(s).

关键词： Deep learning

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CHART-Info 2024: A Dataset for Chart Analysis and recognition 27th

CHART-Info 2024: A Dataset for Chart Analysis and Recognit...

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27th international conference on pattern recognition, ICPR 2024

作者： Davila, Kenny Lazarus, Rupak Xu, Fei Rodríguez Alcántara, Nicole Setlur, Srirangaraj Govindaraju, Venu Mondal, Ajoy Jawahar, C.V. School of Computing DePaul University ChicagoIL60604 United States International Institute of Information Technology Hyderabad India CSE University at Buffalo BuffaloNY14260 United States Facultad de Ingenieria Universidad Tecnológica Centroamericana Tegucigalpa Honduras

ISBN: (纸本)9783031784941

Charts are tools for data communication used in a wide range of documents. Recently, the pattern recognition community has shown interest in developing methods for automatically processing charts found in the wild. Following previous efforts on ICPR’s CHART-Infographics competitions, here we propose a newer, larger dataset and benchmark for analyzing and recognizing charts. Inspired by the steps required to make sense of a chart image, the benchmark is divided into 7 different tasks: chart image classification, chart text detection and recognition, text role classification, axis analysis, legend analysis, data extraction, and end-to-end data extraction. We also show the performance of different baselines for the first five tasks. We expect that the increased scale of the proposed dataset will enable the development of better chart recognition systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Charts Dataset Graphic recognition

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