IEEE Access最新文献_第9页

Unsupervised Anomaly Detection in Hyperspectral Imaging: Integrating Tensor Robust Principal Component Analysis With Autoencoding Adversarial Networks

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-01-27 DOI: 10.1109/ACCESS.2025.3534981

Atsuya Emoto;Ryo Matsuoka

{"title":"Unsupervised Anomaly Detection in Hyperspectral Imaging: Integrating Tensor Robust Principal Component Analysis With Autoencoding Adversarial Networks","authors":"Atsuya Emoto;Ryo Matsuoka","doi":"10.1109/ACCESS.2025.3534981","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3534981","url":null,"abstract":"Hyperspectral (HS) image analysis has gained significant attention due to its ability to capture detailed spectral information across hundreds of bands, making it useful for environmental monitoring and mineral exploration applications. However, detecting anomalies in HS images, especially in complex scenes, remains challenging. This paper proposes a novel approach for robust anomaly detection by integrating tensor robust principal component analysis (TRPCA) with autoencoding adversarial networks (AEAN). Our method utilizes the AEAN model to learn a nonlinear low-dimensional representation of the spectral characteristics of background regions, which is then incorporated into the TRPCA framework. The TRPCA is further enhanced by incorporating prior knowledge of the sparsity of anomalous regions, enabling more accurate separation of background and anomaly components. This integration, achieved through a plug-and-play alternating direction method of multipliers (PnP-ADMM), significantly improves detection accuracy and robustness. Experimental results on benchmark datasets widely used for HS anomaly detection confirm that the proposed method consistently outperforms conventional techniques, achieving superior area-under-the-curve (AUC) scores across diverse and complex scenes. By leveraging both nonlinear modeling of background characteristics and sparsity-based anomaly separation, this research provides a more accurate and robust solution for HS anomaly detection, highlighting its potential for practical applications in remote sensing.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"21422-21433"},"PeriodicalIF":3.4,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10855416","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Understanding and Optimizing Oxygen Plasma Treatment for Enhanced Cu-Cu Bonding Application

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-01-27 DOI: 10.1109/ACCESS.2025.3534169

Sangwoo Park;Sangmin Lee;Junyoung Choi;Sarah Eunkyung Kim

{"title":"Understanding and Optimizing Oxygen Plasma Treatment for Enhanced Cu-Cu Bonding Application","authors":"Sangwoo Park;Sangmin Lee;Junyoung Choi;Sarah Eunkyung Kim","doi":"10.1109/ACCESS.2025.3534169","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3534169","url":null,"abstract":"This study investigates the optimization of O2 plasma treatment conditions to enhance Cu-Cu bonding. The O2 plasma treatment conditions were optimized using Design of Experiments (DOE), adjusting three parameters: O2 flow rate, plasma power, and treatment time, to minimize oxidation while maximizing surface energy. X-ray photoelectron spectroscopy (XPS) was employed to calculate the Cu atomic percentage (at%) at the surface and at a depth of 25 seconds of etching, while water contact angle (WCA) measurements assessed surface energy. The results indicated that decreasing the O2 flow rate reduced oxidation without significantly impacting surface energy. Plasma power and treatment time were optimized through a balanced approach. The identified optimal conditions were an O2 flow rate of 50 sccm, plasma power of 50 W, and a process time of 20 seconds. Subsequent SEM analysis confirmed a wavy bonding interface indicative of strong Cu diffusion bonding, resulting in approximately a 40% increase in shear strength. The findings suggest that controlled O2 plasma treatment effectively enhances bonding strength, providing direction for the optimization of O2 plasma for Cu bonding in advanced packaging technologies and hybrid bonding applications.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"20160-20170"},"PeriodicalIF":3.4,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10854209","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhanced DeepSORT and StrongSORT for Multicattle Tracking With Optimized Detection and Re-Identification

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-01-27 DOI: 10.1109/ACCESS.2025.3535092

Hyeon-Seok Sim;Hyun-Chong Cho

{"title":"Enhanced DeepSORT and StrongSORT for Multicattle Tracking With Optimized Detection and Re-Identification","authors":"Hyeon-Seok Sim;Hyun-Chong Cho","doi":"10.1109/ACCESS.2025.3535092","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3535092","url":null,"abstract":"Recently, labor shortages in the farming industry have increased the demand for automation. Object tracking technology has emerged as a critical tool for monitoring livestock through automated systems. This study focuses on tracking individual cattle using object detection and tracking algorithms. Data were collected noninvasively using cameras, and a tracking-by-detection (TBD) approach was adopted. The proposed framework introduces multiple enhancements optimized for cattle tracking. These enhancements include a comparison of five different bounding box regression losses to improve detection accuracy, modifications to the Kalman filter state vector for more accurate bounding box predictions, and adjustments to the feature vector distance metric in the re-identification algorithm. YOLOv9-t was used as the detector, whereas DeepSORT and StrongSORT served as trackers. Compared with the baseline, which uses DeepSORT, the proposed method achieved significant improvements in higher-order tracking accuracy (HOTA) by 4.1%, multiple object tracking accuracy (MOTA) by 1.08%, and identification F1 score (IDF1) by 5.12%, reaching values of 78.64%, 90.29%, and 91.41%, respectively, while reducing the number of ID switches (IDSW).","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"19353-19364"},"PeriodicalIF":3.4,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10855392","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143184126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Wireless Sensor Network Optimization for Constrained Environments With Limited a Priori Target Information

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-01-27 DOI: 10.1109/ACCESS.2025.3535155

Joseph Mockler;Sarah Wielgosz;Huan Xu

{"title":"Wireless Sensor Network Optimization for Constrained Environments With Limited a Priori Target Information","authors":"Joseph Mockler;Sarah Wielgosz;Huan Xu","doi":"10.1109/ACCESS.2025.3535155","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3535155","url":null,"abstract":"In target localization problems, positioning between sensor networks and targets plays a critical role in localization estimation. Because estimation performance is strongly dependent on sensor-target relative positions, many analytically optimal expressions for sensor positions have been constructed as a function of target locations under ideal circumstance. But as more fields have adopted wireless sensing networks, these analytically-optimal placements are not applicable under physical limitations, including placement constraints, network limitations, and sensor modalities. Similarly, these analytically optimal expressions assume the target position is known a priori, but is not a valid assumption for many practical applications. To address this limitation, this paper defines a procedure for optimally placing sensors in a heterogeneous wireless sensor network for localizing targets without an estimate of the target location a priori. We address this by defining a general region where the target may exist, and characterize the network’s sensing performance using the Fisher information matrix. We consider a heterogeneous network of arbitrary size under distance-dependent noise as we search for and continue to optimally monitor a set of unknown static target positions. We define this optimization problem to generally include physical constraints, which are much more practical for most localization applications. Extensive simulations are conducted under two different environmental monitoring example problems to corroborate our approach and demonstrate its range and flexibility. This work may find application in wireless sensor network planning and design for defense, environmental monitoring, surveillance, or autonomous system applications.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"19545-19559"},"PeriodicalIF":3.4,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10855424","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143183786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Extrapolation of Metal Gate With High-K Spacer in Strained Nanosystem Channel QWB Cylindrical FET for High-Speed Applications

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-01-27 DOI: 10.1109/ACCESS.2025.3534561

Rasmita Barik;Rudra Sankar Dhar;Kuleen Kumar;Yash Sharma;Amit Banerjee

{"title":"Extrapolation of Metal Gate With High-K Spacer in Strained Nanosystem Channel QWB Cylindrical FET for High-Speed Applications","authors":"Rasmita Barik;Rudra Sankar Dhar;Kuleen Kumar;Yash Sharma;Amit Banerjee","doi":"10.1109/ACCESS.2025.3534561","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3534561","url":null,"abstract":"The development of novel strain-engineered channel Cylindrical Gate-All-Around (CGAA) quantum well-barrier (QWB) field-effect transistors (FETs) using high-k gate stacks and metallic gates with varying work functions is analyzed, offering enhanced performance to meet the 1 nm technology node of IRDS 2028. The devices incorporate a QWB system incorporating strain engineering in the ultrathin channel region flanked by high-k spacers surrounding the underlaps and metal gate with a stack high-k dielectric. Key electrostatic characteristics, including the Ion/Ioff ratio, leakage current, on-current, sub-threshold swing (SS), drain-induced barrier lowering (DIBL), and transconductance, were extrapolated and analyzed for the CGAA FETs developed in this study. The tungsten metal gate device provides a significantly improved Ion/Ioff ratio with a notable 98.18% decrease in the off-current and 22.5% increase in the ON current, in contrast to existing cylindrical GAA FET. In addition, the novel strain-engineered channel CGAA QWB FET (Device C), which has a higher metal gate work function, is endorsed for near-optimal SS with augmented transconductance. The output performance (ID-V<inline-formula> <tex-math>$_{mathrm {DS}}$ </tex-math></inline-formula>) resolves a huge enhancement in contrast to the existing GAA and IRDS 2028 1 nm technology node criteria. Hence, the device (nanowire-strained channel QWB CGAA FET) with a tungsten gate is better suited for low-power, high-speed applications with minimal short-channel effects, and is the device of future connecting numerous RF and digital applications as well as faster switching speed.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"19469-19483"},"PeriodicalIF":3.4,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10854207","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143184390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

D2CBDAMAttUnet: Dual-Decoder Convolution Block Dual Attention Unet for Accurate Retinal Vessel Segmentation From Fundus Images

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-01-27 DOI: 10.1109/ACCESS.2025.3535239

Vo Trong Quang Huy;Chih-Min Lin

{"title":"D2CBDAMAttUnet: Dual-Decoder Convolution Block Dual Attention Unet for Accurate Retinal Vessel Segmentation From Fundus Images","authors":"Vo Trong Quang Huy;Chih-Min Lin","doi":"10.1109/ACCESS.2025.3535239","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3535239","url":null,"abstract":"Gathering detailed morphological information from retinal blood vessels is crucial in clinical diagnostics, enabling doctors to make precise assessments of patient conditions and to devise custom treatments. Traditional methods of segmenting these vessels from fundus images are not only tedious but require a high degree of specialized knowledge. In light of this, Deep Convolutional Neural Networks (DCNNs), particularly those based on the U-Net architecture, have been acknowledged for their effectiveness in capturing and utilizing contextual features within this context. However, these methods often grapple with challenges such as loss of vital information during pooling and insufficient handling of local context in skip connections, leading to less than optimal results. To address these limitations, this research propounds the novel Convolution Block Dual Attention Module (CBDAM), as well as two pioneering network architectures: The Convolution Block Dual Attention Module Unet (CBDAMUNet) and the Dual Decoder Convolution Block Attention Module with Attention U-Net (D2CBDAMAttUnet). These are built upon a fortified encoder-decoder structure aimed at providing an automated, streamlined detection mechanism from fundus imagery. Thoroughly tested against the DRIVE, CHASEDB1 and STARE datasets, recognized standards in retinal vessel segmentation, the proposed models not only redefine the accuracy benchmarks but also represent a significant stride in automated retinal vessel analysis. The introduction of these advanced networks is a milestone in ophthalmologic diagnostics and research, offering a potent asset for medical professionals and specialists in the field.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"19635-19649"},"PeriodicalIF":3.4,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10855415","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unified Visual-Aware Representations for Data Analytics

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-01-27 DOI: 10.1109/ACCESS.2025.3534330

Ladislav Peška;Ivana Sixtová;David Hoksza;David Bernhauer;Jakub Lokoč;Tomáš Skopal

{"title":"Unified Visual-Aware Representations for Data Analytics","authors":"Ladislav Peška;Ivana Sixtová;David Hoksza;David Bernhauer;Jakub Lokoč;Tomáš Skopal","doi":"10.1109/ACCESS.2025.3534330","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3534330","url":null,"abstract":"One of the characteristics of big data is its internal complexity and variety manifested in many types of datasets that are to be managed, searched, or analyzed. In their natural forms, some data entities are unstructured, such as texts or multimedia objects, while some are structured but too complex (e.g., high-dimensional tabular data). Due to the many different forms of data managed in many domain-specific problems, there are many different data representations used – tailored to a specific data form, domain and task. In this paper, we propose a framework for universal visual representations of complex data. The desired property of the visualizations is the ability to visually encode the semantic features of the original data. Hence, processing of visualizations (images) by generic deep learning models results in deep feature vectors that could be uniformly used in standard data retrieval/analytics tasks. Specifically, we develop a semi-automated transfer learning pipeline for transformation of input arbitrary tabular data into visual representations. The visual representations serve for data analytics tasks performed by human users as well as serve for universal data representations used in machine learning models for automated tasks. We show in large study that visual representations of complex data are effective in a number of domains while we also propose a recommender to help with the parameterization of the entire pipeline for certain domains and use cases. In summary, the proposed framework enables rapid prototyping of data representations (in an arbitrary domain) using a shared concept – visual representations applicable in data analytics using generic deep learning models.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"19694-19715"},"PeriodicalIF":3.4,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10854212","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Re-Calibrating Network by Refining Initial Features Through Generative Gradient Regularization

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-01-27 DOI: 10.1109/ACCESS.2025.3534216

Naim Reza;Ho Yub Jung

{"title":"Re-Calibrating Network by Refining Initial Features Through Generative Gradient Regularization","authors":"Naim Reza;Ho Yub Jung","doi":"10.1109/ACCESS.2025.3534216","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3534216","url":null,"abstract":"In the domain of Deep Neural Networks (DNNs), the deployment of regularization techniques is a common strategy for optimizing network performance. While these methods have been shown to be effective for optimization, they typically necessitate complete retraining of the network. We propose a training methodology that emphasizes on refining the features extracted from the initial layer of a DNN by regularizing the network with the help of a reference gradient. Our findings indicate that augmenting the gradients produced by the filters of the initial layer of a DNN, through the introduction of a reference gradient, leads to refined feature extraction and enhanced performance. We produce the reference gradient from the decoder of a generative network and subsequently encourage the target classifier network to adjust its weights to minimize discrepancies between the reference gradient and the gradient produced by the classifier network. The experiments show that implementing this method on a pre-trained network effectively re-calibrates the network and augments higher variance filters of the initial layer of the network, which helps produce refined features. Notably, this refinement in features translates to improved generalization and the proposed method also eliminates the necessity of total retraining of the target network. In empirical evaluation, we applied the proposed methodology to CIFAR, SVHN and ImageNet datasets, utilizing a range of network architectures. The results evidenced a performance gain of 1.66% for the CIFAR dataset using WideResNet, 1.22% for the SVHN dataset using PreResNet and 0.57% for the ImageNet dataset using ResNet.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"20191-20202"},"PeriodicalIF":3.4,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10854681","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Consensus-Based Resilience Assurance for System of Systems

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-01-27 DOI: 10.1109/ACCESS.2025.3535519

Huanjun Zhang;Yutaka Matsubara

{"title":"Consensus-Based Resilience Assurance for System of Systems","authors":"Huanjun Zhang;Yutaka Matsubara","doi":"10.1109/ACCESS.2025.3535519","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3535519","url":null,"abstract":"The complexity of a System of Systems makes resilience one of its key attributes. Numerous studies have focused on the quantitative assessment of resilience by trailing indicators, yet discussions on resilience assurance through monitoring leading indicators remain scarce. Resilience assurance in SoS faces two major challenges: lack of structured argumentation work related to resilience and conflicts among multiple independent stakeholders. To address these challenges, this paper first introduces a resilience argumentation approach based on STAMP (Systems-Theoretic Accident Model and Processes), then employs cooperative consensus process model to seek consensus on resilience assurance. Additionally, under the requirements of the international standard IEC 62853 for open systems dependability, a consensus based resilience assurance framework is proposed. Within the framework, the resilient team can discuss the specific implementation details of failure response, accountability, and change accommodation based on stakeholder consensus. Finally, two SoS case studies, Microgrid and Mobility as a Service, are used to demonstrate the application of the proposed approach.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"20203-20217"},"PeriodicalIF":3.4,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10855432","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhanced Emotion-Aware Conversational Agent: Analyzing User Behavioral Status for Tailored Reponses in Chatbot Interactions

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-01-27 DOI: 10.1109/ACCESS.2025.3534197

S. Abinaya;K. S. Ashwin;A. Sherly Alphonse

{"title":"Enhanced Emotion-Aware Conversational Agent: Analyzing User Behavioral Status for Tailored Reponses in Chatbot Interactions","authors":"S. Abinaya;K. S. Ashwin;A. Sherly Alphonse","doi":"10.1109/ACCESS.2025.3534197","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3534197","url":null,"abstract":"While chatbots are increasingly popular for communication, their effectiveness is limited by their difficulty in understanding users’ emotions. To address this, this study proposes a new hybrid chatbot model called “TEBC-Net” (Text Emotion Bert CNN Network), which combines text and video analysis to interpret user emotions and generate more empathetic responses. At the core of TEBC-Net is a multi-modal emotion analysis system. One component uses Bidirectional Encoder Representations from Transformers (BERT), a well-regarded model in natural language processing (NLP), achieving an 87.21% accuracy rate in detecting emotional cues from text inputs. The second component captures users’ facial expressions through webcam footage. It begins by detecting faces using a pre-trained classifier like Haarcascade. Then, to improve emotion recognition, it preprocesses the image through brightness adjustments and contrast enhancement with Automatic CLAHE and dual gamma correction. This processed image is analyzed by a Convolutional Neural Network (CNN) model trained specifically for emotion recognition, reaching 74.14% accuracy by assigning probabilities to different emotions. By integrating insights from both text and video analysis, TEBC-Net gains a comprehensive understanding of the user’s emotional state and intent. This combined data then informs the chatbot’s response generation module, enabling it to craft responses that are both empathetic and more directly aligned with the user’s emotional needs.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"19770-19787"},"PeriodicalIF":3.4,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10854433","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0