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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1-10 订阅

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Proceedings - 2024 5th international conference on machine learning and human-computer interaction, mlhmi 2024

Proceedings - 2024 5th International Conference on Machine L...

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5th international conference on machine learning and human-computer interaction, mlhmi 2024

ISBN: (纸本)9798350375763

the proceedings contain 9 papers. the topics discussed include: categorization of post-sale automotive customer feedback using natural language processing;improving service chatbot using semantic based short-term memory;design of pig inventory and abnormality monitoring system based on livestock Internet of things orbital inspection robot;a study on the cognition of symbolic imagery in computer graphics synthetic imagery;and breaking the validation trade-off in topic extraction: a bi-objective metaheuristic model for labelling short-text clusters and an application on AirBnB Tokyo reviews.

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Categorisation of post-sale automotive customer feedback using Natural Language Processing 5

Categorisation of post-sale automotive customer feedback usi...

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5th international conference on machine learning and human-computer interaction (mlhmi)

作者： Vicente-Estevez, Diego Dacal-Nieto, Angel Paradela, Andres Alonso-Ramos, Victor Jose Areal, Juan Riomao, Alberto CTAG Ctr Tecnol Automoc Galicia O Porrino Spain Stellantis Espana Vigo Spain

ISBN: (纸本)9798350375770;9798350375763

Automotive manufacturing is permanently searching new methods to improve its efficiency and quality. A relevant data source to do this is post-sale customer feedback. they can highlight situations in manufacturing processes not detected in the factory, that may repeat in the future. the analysis of these data is currently performed manually by experts, which try to label each new occurrence with known potential causes, in a long and inefficient process. this paper proposes a new system to analyse customer feedback in order to categorise it from their raw description, aiming to facilitate and boost problem solving in the related manufacturing process. It is based in the usage of Natural Language Processing and machine learning algorithms, since most of its description is based on non-standardized text fields, and in different languages. the solution has been validated with Stellantis automotive group data, obtaining that a combination of Decision Trees and Gradient Boosting classifiers provides an acceptable performance of 85% of accuracy, acting as a first solution to a very complex problem, with high potential of impact in the manufacturing efficiency.

关键词： Natural Language Processing NLP Artificial Intelligence AI automotive manufacturing machine learning

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Performance and Reliability of Touch Screen: A Game -Oriented Research 5

Performance and Reliability of Touch Screen: A Game -Oriente...

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5th international conference on machine learning and human-computer interaction (mlhmi)

作者： Fu, Rongrong Dong, Qianzhou Liu, Yuwei China Acad Informat & Commun Technol Beijing Peoples R China

ISBN: (纸本)9798350375770;9798350375763

With the rapid development of the mobile gaming industry, mobile devices have emerged as imperative platforms for gaming. Touch interaction has gained significant traction within the gaming industry, underscoring the need to scrutinize its performance and reliability to enhance the overall user experience. However, the existing research on touch interaction in gaming scenarios remains limited. this study strives to conduct a thorough investigation into the performance and reliability of touch screens in gaming environments through empirical experimentation. Primarily, a comprehensive set of experimental equipment and testing methods are proposed. then, key performance and reliability indicators pertaining to touch interaction are introduced. To facilitate the research, two distinct genres of high-load online mobile games are selected. Ultimately, the study measures the performance and reliability of touch interaction on smartphones and tablets through gaming scenarios. Our experiments show that, in gaming scenarios, the average click latency of mobile devices ranges from 94ms to 162ms, and the average slide latency ranges from 116ms to 187ms. Under 100 consecutive shooting operations, the continuous click accuracy rate remains above 96%. Additionally, we tested one million touch interactions on two Android phones and encountered only 5 and 10 anomaly cases, respectively.

关键词： Touch interaction Performance Reliability Mobile game Latency

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A Study on the Cognition of Symbolic Imagery in computer Graphics Synthetic Imagery 5

A Study on the Cognition of Symbolic Imagery in Computer Gra...

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5th international conference on machine learning and human-computer interaction (mlhmi)

作者： Yu-Che, Huang Ping-Hsien, Huang Chaoyang Univ Technol Dept Ind Design Taichung Taiwan Natl Taiwan Univ Sci & Technol Dept Mech Engn Taipei Taiwan

ISBN: (纸本)9798350375770;9798350375763

In computer graphics, symbolic imagery plays a vital role and has an inevitable impact on creation, communication and emotional expression. through symbolic imagery, we are able to convey complex concepts and emotions, while also providing users with a wealth of meaningful information and a rich user experience. Here are just a few of the effects of computer-drawn symbolic imagery: Symbolic imagery can convey complex concepts and messages using simple graphics or images. For example, icons and logos are often used to quickly convey the functionality of an app or website, helping users quickly understand its purpose. In addition, symbolic imagery plays a key role in brand building. A unique and distinctive logo can help differentiate a brand from its competitors and establish a brand image in the minds of users. these symbolic images may be seen in logos, advertising, and product design. this research will focus on the image cognitive characteristics of ordinary people through the integration of computer image synthesis technology. In user interface design, symbolic imagery is used to improve the usability and operability of the system. Observed symbolic imagery can help quickly understand how users operate, thereby increasing usage and user satisfaction. Finally, I hope that computer graphics will not only be part of the visual design, but also a way of communication and expression, which can profoundly affect the user's experience and product acceptance. In the design process, the rational use of symbolic imagery will help enhance the value and appeal of the product. We hope to provide a reference for future machine learning through the study of computer images through image symbol recognition

关键词： Symbolic image cognition1 computer image synthesis2 image item feature cognition 3

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Hand Gesture-Driven Gaming for Effective Rehabilitation and Improved Quality of Life - A Review 5

Hand Gesture-Driven Gaming for Effective Rehabilitation and ...

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5th international conference on Innovative Trends in Information Technology (ICITIIT)

作者： Sophiya, E. Reddy, Singam Sindhuja Amrita Vishwa Vidyapeetham Dept CSE Amrita Sch Comp Chennai India

ISBN: (纸本)9798350386813;9798350386820

Hand gesture-driven gaming is a new technology that has arisen as a dynamic and inventive technique to cater to people in therapy, recovery from nerve problems, and rehabilitation. these are intended to be inclusive, allowing people of varied physical abilities to participate in immersive gaming experiences. through tracking hand motions and interpreting gestures, we can play the games without any use of hardware and this works with intuitive human-computer interaction (HCI). this research work provides an in-depth analysis of recent research papers and efforts on hand gesture-driven gaming systems, which uses computer vision techniques such as OpenCV and deep learning models to track hand positions and recognize specific motions in order to play games.

关键词： Hand gesture-driven gaming computer vision hand gestures OpenCV deep learning machine learning gaming rehabilitation human-computer interaction (HCI)

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Reward Adjustment Mechanism for PUBG Background MAS 5

Reward Adjustment Mechanism for PUBG Background MAS

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

作者： Xie, Jinxiang School of Xiamen University Malaysia Sepang Malaysia

ISBN: (纸本)9798350368284

this paper presents the design and implementation of a dynamic reward mechanism for game multi-Agents in a two-dimensional simulation environment inspired by the popular game PUBG. the research focuses on improving the learning efficiency and strategic behaviour of agents by dynamically adjusting the reward system based on environmental cues and intelligence performance. this paper proposes a two-Tier dynamic reward mechanism, which balances individual and team incentives, with the aim of promoting competitive and cooperative behaviors in highly dynamic and competitive game scenarios. © 2024 IEEE.

关键词： Adversarial machine learning

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Application of XGBoost Model for Predicting Isobaric Heat Capacity of Liquid HC, HFC, and HFO Refrigerants 5

Application of XGBoost Model for Predicting Isobaric Heat Ca...

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

作者： Dong, Shaoli School of Energy and Power Engineering Chongqing University Key Laboratory of Low-Grade Energy Utilization Technology and System Ministry of Education Chongqing400044 China

ISBN: (纸本)9798350368284

this article presents a machine learning model for accurately calculating the isobaric heat capacity of pure HC, HFC, and HFO refrigerants in the liquid phase. Initially, a comprehensive evaluation was conducted on 1687 experimental data points encompassing various refrigerants. Subsequently, distinct linear algorithms, nonlinear algorithms, and ensemble algorithms were individually trained and tested. After careful comparison and analysis, XGBoost emerged as the most effective algorithm for this task. the utilization of XGBoost in predicting and computing the isobaric heat capacity of refrigerants demonstrated excellent agreement with experimental values. the resulting R 2 value was found to be 0.9734 while RMSE stood at 0.1267 {kJ}/({K}). © 2024 IEEE.

关键词： Adversarial machine learning

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Application of Deep learning-Based User Intent Recognition in human-computer interaction 5

Application of Deep Learning-Based User Intent Recognition i...

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

作者： Qiu, Lingling Nanchang Institute of Technology Nanchang333000 China

ISBN: (纸本)9798350368284

With the rapid development of human-computer interaction (HCI) technology, accurately recognizing user intent has become crucial for enhancing user experience. this paper focuses on deep learning-based user intent recognition technology and explores its innovative applications in HCI. We first review recent advances in deep learning in the field of natural language processing, particularly its superior performance in user intent recognition tasks. By constructing deep learning models such as Recurrent Neural Networks (RNNs), Long Short-Term Memory Networks (LSTMs), and Transformers, this research aims to extract high-level features from user input data including text, speech, or behavioral data to achieve precise user intent recognition. © 2024 IEEE.

关键词： Contrastive learning

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Versatile Wearable Interface: Multi-modal Tactile Sensing and Haptic Feedback for human-computer interaction 5th

Versatile Wearable Interface: Multi-modal Tactile Sensing an...

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5th international conference on Vibration and Energy Harvesting Applications-VEH

作者： Liu, Ming Chen, Tao Zhu, Minglu Soochow Univ Sch Mech & Elect Engn Jiangsu Prov Key Lab Adv Robot Suzhou 215123 Peoples R China Soochow Univ Sch Future Sci & Engn Suzhou 215123 Peoples R China

ISBN: (纸本)9789819611935;9789819611911;9789819611904

human-machine interaction is an important aspect of exchanging information between the virtual reality world and the physical world. Haptic information interaction enables users to have immersive and efficient interactions, enhancing the overall experience. With the increasing number of tactile sensors and actuators in human-machine interaction systems, there are new requirements for multifunctional integration, lightweight energy consumption, and portability of devices. this paper proposes a human-machine interaction interface based on multi-modal haptic sensing and feedback fusion devices. this human-machine interaction interface has the following functions: touch-slip sensation sensing based on triboelectric-piezoelectric hybrid effect, vibration feedback, and pneumatic pressure feedback. We apply this human-machine interaction interface to robot digital twin interaction control, providing users with a more realistic control experience through the integration of haptic sensing and feedback.

关键词： Pneumatic actuator Haptic feedback Triboelectric sensor Piezoelectric actuator human-machine interface

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Research on Big Data Financial Fraud Detection System Based on machine learning 5

Research on Big Data Financial Fraud Detection System Based ...

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

作者： Li, Yutong School of Management and Innovation SciencesPo Paris Paris75007 France

ISBN: (纸本)9798350368284

To solve the problem of fraud detection in financial transaction data, this paper proposes an innovative detection scheme that integrates machine learning and big data analysis technology. We combine the subtleties of time series analysis with the power of modern data analysis to design a model that is both efficient and accurate in identifying financial fraud. First, advanced data acquisition technology is used to capture high-frequency financial transaction data in real time to ensure the freshness and integrity of data. then, the ARIMA (autoregressive integral moving average) model is used to analyze these time series data in depth to reveal the hidden information behind the trading patterns and trends. On this basis, the LightGBM (Lightweight Gradient Elevator) algorithm is introduced, which is a machine learning tool that performs well when dealing with high-dimensional, sparse data, especially for complex data structures in the financial sector. By integrating the time series features mined by ARIMA with multi-dimensional information such as credit history and geographical location of both parties, as the input of LightGBM model, we construct a multi-modal fraud detection framework. Empirical research shows that compared with using ARIMA model alone or traditional machine learning methods, our hybrid model has achieved a significant breakthrough in the accuracy and response speed of fraud detection, which can not only perceptively capture abnormal transaction activities, but also proactively warn potential financial risk points. © 2024 IEEE.

关键词： Data integrity

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