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检索条件"任意字段=5th International Conference on Machine Learning and Human-Computer Interaction, MLHMI 2024"

共 548 条记录，以下是71-80 订阅

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Virtual White Board using Hand Gestures 5

Virtual White Board using Hand Gestures

引用

5th international conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Patil, Manjiri Patil, Palak Deshpande, Vedashree Patil, Tanvi St. John College Of Engineering And Management Department of Information Technology Palghar India

ISBN: (纸本)9798350389609

With the help of hand gestures, the study presents a novel virtual whiteboard system that improves user engagement. Users can operate the virtual whiteboard interface with ease. this was all possible due to the system's usage of computer vision technology, which interprets and reacts to dynamic hand movements. the suggested system allows users to draw, erase, and alter content on the virtual canvas without making physical contact by integrating depth-sensing cameras and gesture recognition algorithms. this creates a natural and immersive experience. Because it encourages a more flexible and dynamic atmosphere for collaboration, the gesture-based interface is especially well-suited for learning environments, corporate gatherings, and innovative ideation sessions. the study assesses the accuracy, responsiveness, and user satisfaction of the system, offering insights into its possible uses and resolving issues related to gesture-based interaction. Future advancements in human-computer interaction can benefit from this research, which also advances the development of virtual collaboration tools that are easy to use. According to a recent research with 200 participants, the new technology proved to be beneficial in supporting dynamic and interactive presentations as seen by the 35% increase in user satisfaction and the 28% improvement in knowledge retention. © 2024 IEEE.

关键词： computer Vision Hand Gestures human-computer interaction

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Enhancing Polyphonic Character Disambiguation in Chinese Text Processing with Bidirectional LSTM Networks 5

Enhancing Polyphonic Character Disambiguation in Chinese Tex...

引用

5th international conference on Intelligent Computing and human-computer interaction, ICHCI 2024

作者： Li, Xuan Sun, Yu School of Computer Science and Technology Changchun University Jilin Changchun China School of Special Education Changchun University Changchun Jilin China

ISBN: (纸本)9798350368284

this study addresses the challenge of polyphonic character ambiguity in Chinese text processing through the development of a deep learning model capable of automatically identifying and resolving such ambiguities. the research begins with data collection and preprocessing, involving the acquisition of polyphonic character text data and a Chinese polyphonic character library, transformed into a suitable format for model input. Subsequently, a model architecture based on a bidirectional Long Short-Term Memory (LSTM) network was designed to enhance semantic understanding using 300-dimensional word embedding vectors. While achieving 82.98% accuracy on the test set, the model encountered high loss values during training, indicating potential issues like gradient vanishing. therefore, future efforts will concentrate on refining the model structure and training methodology to enhance numerical stability and generalization capabilities, aiming for improved performance in real-world polyphonic character disambiguation tasks. © 2024 IEEE.

关键词： Semantics

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Real-time Facial Expression Recognition Research Based on Blazeface Face Detection and Resnet Emotion Classification 5

Real-time Facial Expression Recognition Research Based on Bl...

引用

5th international conference on computer Vision, Image and Deep learning, CVIDL 2024

作者： Zhu, Zhenting Jiao, Rui The University of Sydney School of Electrical and Computer Engineering Sydney Australia Co Ltd. Beijing China

ISBN: (纸本)9798350373820

Facial expression recognition technology has the potential to enhance computers' ability to comprehend and identify emotions, thereby improving human-computer interaction. In response to the real-time detection challenges encountered in current research, this paper developed a facial expression recognition system based on deep learning. Firstly, OpenCV is employed for preprocessing the acquired facial images, and the Blazeface face detector is utilized to provide the coordinate information of the faces within the images. Secondly, the paper utilizes the Resnet18 neural network to train the Fer2013 facial expression dataset for the purpose of developing a facial emotion classifier. Finally, the developed facial emotion classifier was utilized for the recognition of seven fundamental emotional states (anger, disgust, fear, happiness, neutrality, sadness, surprise), and the performance of the classifier was assessed and validated using publicly available expression databases. the experimental findings suggest that Blazeface face detection and Resnet emotion classification are appropriate for real-time detection of facial expressions. © 2024 IEEE.

关键词： Face recognition

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Roughness detection based on multiple lighting conditions and deep learning 5

Roughness detection based on multiple lighting conditions an...

引用

5th international conference on computer Vision, Image and Deep learning, CVIDL 2024

作者： Feng, Mingchi Huang, Jinyu Zhang, Jun Zhen, Yan Chongqing University of Posts and Telecommunications School of Advanced Manufacturing Engineering Chongqing China

ISBN: (纸本)9798350373820

In surface roughness measurement, traditional machine vision detection methods are based on human-designed image feature indicators, which requires to manual intervention and have low efficiency. therefore, this paper presents a method for measuring surface roughness based on a convolutional neural network. this method utilizes ResNet50 to automatically extract multi-scale image features. through the bidirectional feature fusion module, different scale feature information is fused. Finally, processing the rich feature information after fusion through fully connected layers regresses accurate roughness values. In addition, the influence of lighting conditions on roughness measurement is analyzed, such as the wavelength and incidence angle of the laser. In our self-generated dataset, the best lighting condition is achieved using the red laser with a wavelength of 658 nm and an angle of incidence of 60°. the corresponding neural network model achieved an average accuracy of 95.9% on the test set. © 2024 IEEE.

关键词： Feature extraction

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MedHAI: Improved Framework for medical diagnosis using human experts 5

MedHAI: Improved Framework for medical diagnosis using human...

引用

5th IEEE India Council international Subsections conference, INDISCON 2024

作者： Gunika Sangal, A.L. National Institute of Technology Jalandhar Department of Computer Science and Engineering India

ISBN: (纸本)9798350376753

While machine learning (ML) models can be trained on large amounts of data, their applicability in critical domains such as cyber-attacks and clinical applications is still limited. Recent research suggests that ML models' predictions when combined with those of human experts, a technique known as human-in-the-loop improves model performance. Due to their complementary strengths, combining them for critical areas like medical, self-driving cars proves beneficial. Also, human experts are scarce and costly, hence it is not possible to take expert advice or annotation for every instance. We propose an approach, which we call MedHAI (Medical human in AI loop) to reduce reliance on human experts by deferring decisions only when confidence levels for machine learning models' prediction are low and when probabilities fall beyond dynamically determined threshold ranges. the pretrained AI model produces calibrated probabilistic outputs and dynamically determines whether to accept the classifier's output or defer to human expertise based on probability thresholds and calibrated confidence levels. the expert advice along with image are then used to predict the output. this combined model using human-in-the-loop uses calibration and improves reliability in terms of accuracy by about 6% on ChestX-ray8 dataset. © 2024 IEEE.

关键词： calibration deep learning disease detection human AI human-in-the-loop

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Detecting Feet's Bending Patterns With Smart Insoles 5

Detecting Feet's Bending Patterns With Smart Insoles

引用

5th IEEE international conference on Pattern Recognition and machine learning, PRML 2024

作者： Zhong, Yuchen Ying, Qijun Cai, Xiaohui dept. of Computer Science and Technology Hefei China dept. of Data Science Hefei China

ISBN: (纸本)9798350355925

Foot bending is a basic component of walking. Although bending is important for foot-ground interaction and ensures the speed and balance of the whole walking cycle, there is still no practical method for recording and analyzing foot bending under everyday scenarios. In this study we propose to use a smart insole, with two embedded Inertial Measurement Units, to acquire the movements of the fore-foot and the hind-foot and deduce the bending, represented by the bending point's position. We use the deep learning approach to estimate the bending position and its changes during different walking patterns. On the dataset containing 8 test subjects and 6 walking patterns, our method can achieve an overall mean absolute error accuracy of 2.58 cm. the smart insole can be with the subject all the day, bring in a new method for continuous foot bending monitoring. © 2024 IEEE.

关键词： Wearable technology

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Chinese Numeral Interface Design of Calculator

Chinese Numeral Interface Design of Calculator

引用

machine learning and human-computer interaction (mlhmi), international conference on

作者： Tsu-wu Hu Johan Lin Department of Industrial Design Chaoyang University of Technology Taichung Taiwan Department of Industrial Design Chaoyang University of Technology Taichung Taiwan

ISBN: (数字)9798350375763

ISBN: (纸本)9798350375770

Traditional calculators displaying Arabic numerals are more aligned with English grammar conventions, whereas users in Chinese character regions have significantly different habits in reading and describing numbers compared to the Western counterparts. this study first refers to design principles mentioned in graphical interface design works, then incorporates features of vertical reading and ideographic characters into the redesign of calculator interfaces. Subsequently, experiments were conducted with 10 participants using both Arabic numeral and Chinese character calculators to compare their convenience and speed. Time was recorded and statistically analyzed. the conclusion drawn from the experiment results indicates that, under general arithmetic operations, the Chinese character interface design offers more convenient unit conversion features and a number display suitable for Chinese readers, resulting in higher convenience and response speed for users in Chinese character regions. In the future, interface designs originally based on English and Arabic numerals can refer to this study's results for adjustments and optimization for Chinese character users.

关键词： human computer interaction Calculators machine learning Calculus Grammar Usability Optimization

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A review of deep learning based methods for 2D human pose estimation 5

A review of deep learning based methods for 2D human pose es...

引用

5th international conference on computer Vision and Data Mining, ICCVDM 2024

作者： Yang, Bo Zhang, Hong Hou, Xiaodong Tao, Qing Lv, Chunguang Zhao, Min Li, Wei Xinjiang Jiruixiang Technology Stock Limited Company Postdoctoral Research Workstation Xinjiang Changji831100 China Xinjiang University Xinjiang Urumqi830000 China

ISBN: (纸本)9781510682863

human posture estimation is an important content in computer vision, which is the research basis of action recognition, human-computer interaction and other directions, and the traditional manual feature approach has certain problems in terms of accuracy, robustness and computation. In recent years, deep learning-based pose estimation methods have achieved high performance in human pose estimation. According to the number of people, this paper classifies deep learning-based 2D human pose estimation methods into single-person pose estimation methods and multi-person pose estimation methods, and summarizes and analyzes each method. A brief overview of the current mainstream datasets and evaluation metrics is provided. Finally, the development of 2D human pose estimation methods is summarized and possible future trends are listed. © 2024 SPIE.

关键词： machine vision

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Dynamic Identification and Optimization of Key Opinion Leaders in Social Networks Using Advanced GCN Models 5

Dynamic Identification and Optimization of Key Opinion Leade...

引用

5th international conference on machine learning and computer Application, ICMLCA 2024

作者： Chen, Yinuo Shenzhen International Exchange Academy Shenzhen China

ISBN: (纸本)9798331530334

the rapid evolution of social media platforms has significantly increased the speed and reach of information dissemination, making the identification of Key Opinion Leaders (KOLs) crucial for shaping public opinion and maximizing influence. this paper presents a novel approach to identifying KOLs through the construction of dynamic social networks, employing advanced Graph Convolutional Networks (GCNs) and a pseudo-labeling mechanism. Pseudo-labeling is an innovative technique that automatically generates labels for nodes in a social network based on their characteristics, such as connectivity and influence level. By categorizing nodes into leader and edge nodes, we enhance the training data available for the dynamic GCN model, enabling it to better capture the underlying relationships within the network. By transforming user interaction data into a weighted social network and applying a community detection algorithm, we effectively generate a pool of candidate opinion leaders. the integration of dynamic GCN models, optimized through a Mahalanobis distance-based similarity measure, further refines the selection process, ensuring that the most influential leaders are identified. Experimental results on real-world datasets demonstrate that our method, outperforms existing algorithms in influence spread and leader identification accuracy, achieving a 12.35% improvement in KOL identification and enhancing precision in influence propagation and community detection, especially with fewer KOLs or clusters. © 2024 IEEE.

关键词： Labeled data

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AD-Aligning: Emulating human-Like Generalization for Cognitive Domain Adaptation in Deep learning 5

AD-Aligning: Emulating Human-Like Generalization for Cogniti...

引用

5th international conference on Electronic Communication and Artificial Intelligence, ICECAI 2024

作者： Li, Zhuoying Wan, Bohua Mu, Cong Zhao, Ruzhang Qiu, Shushan Yan, Chao Johns Hopkins University Department of Computer Science Baltimore United States University of Houston Department of Electrical and Computer Engineering Houston United States Northeastern University Department of Electrical and Computer Engineering Sunnyvale United States

ISBN: (纸本)9798350386943

Domain adaptation is pivotal for enabling deep learning models to generalize across diverse domains, a task complicated by variations in presentation and cognitive nuances. In this paper, we introduce AD-Aligning, a novel approach that combines adversarial training with source-target domain alignment to enhance generalization capabilities. By pretraining with Coral loss and standard loss, AD-Aligning aligns target domain statistics with those of the pretrained encoder, preserving robustness while accommodating domain shifts. through extensive experiments on diverse datasets and domain shift scenarios, including noise-induced shifts and cognitive domain adaptation tasks, we demonstrate AD-Aligning's superior performance compared to existing methods such as Deep Coral and ADDA. Our findings highlight AD-Aligning's ability to emulate the nuanced cognitive processes inherent in human perception, making it a promising solution for real-world applications requiring adaptable and robust domain adaptation strategies. © 2024 IEEE.

关键词： Adversarial machine learning

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