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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 558 条记录，以下是271-280 订阅

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5th international conference on HCI in Games, HCI-Games 2023, held as part of the 25th international conference on human-computer interaction, HCII 2023

5th International Conference on HCI in Games, HCI-Games 2023...

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5th international conference on HCI in Games, HCI-Games 2023, held as part of the 25th international conference on human-computer interaction, HCII 2023

ISBN: (纸本)9783031359293

the proceedings contain 54 papers. the special focus in this conference is on HCI in Games. the topics include: Toward Computationally-Supported Roleplaying for Perspective-Taking;critical Success Factors to Evaluate Startup Success;A More Reasonable Mecha Design Approach in AI – Mecha Characters with Tang Dynasty Elements as an Example;Driving Consumer Value Co-creation and Repurchase Intention and e-WOM by Gameful Experience in Gamification;"Escapad" Dance Serious Game: Designing a therapeutic Tool for Elderly with Mild Cognitive Impairment;using MediaPipe machine learning to Design Casual Exertion Games to Interrupt Prolonged Sedentary Lifestyle;rethinking Gamification through Artificial Intelligence;serious Game Design for High-Risk Training – the Case of Offshore Wind Turbine Maintenance Personnel Training;fits Like a Game: A Multi-criteria Adaptive Gamification for Collaborative Location-Based Collecting Systems;games as Cinematic Experiences: Discussing Filmic Modes and Ludic Elements in Video-Game Storytelling;student’s View on Hate Speech: Gamified Annotation for Educational Use;verification of Repetition of Playing a Game for Activity Selection in a Group;systematic Review: Incorporating Serious Games into Public Environmental Education on Urban Wildlife Issues;using Gamification to Activate University Library Use;orpheus’ Journey;prototyping Gamification Apps Using Board Games: A Case Study of Muscle Stretching App;Empathy Game: Using the Game as SPRINT Warm-Up Process in a Cross-Disciplinary Team Co-creation;Research on the Design of Gamified Cervical Spine Exercise APP Based on Octalysis;exploring the Efficacy of Persuasion Strategies for Different User Types in Games that Encourage Environmentally Behavior;a Survey on Application of Game Design Element in Edutainment;level Flow Patterns in Game Design;foreword.

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Unveiling Stock Market Trends through Predictive Analytics and Sentiment Analysis: InsightfulEquity 5

Unveiling Stock Market Trends Through Predictive Analytics a...

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5th IEEE international conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Gupta, Amit Kumar Kumar, Vipin Verma, Ankit Yadav, Prakriti Kumar, Nikhil Sain, Mangal Kiet Group of Institutions Department of Computer Applications Delhi NCR India Integrated Program in Management Indian Institute of Management Indore Madhya Pradesh Indore India Dongseo University Division of Computer and Information Engineering Busan Korea Republic of

ISBN: (纸本)9798350383522

In the world of economic markets, it is nevertheless now not clean to as it should be decide stock expenses. Our research objectives to go deeper into financial savings forecasting by way of developing innovative forecasting strategies and superior emotional guide assessment techniques. By combining those techniques, we hope to better recognize the complicated dating between stock prices and social media sentiment. the three fashions blanketed in our evaluation are Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM) networks, and Linear Regression. In the present examine, we use statistical trying out, regression analysis, and correlation models to have a look at the predictive properties of these fashions at special time points and reveal unique accuracy indices for every. On the other hand, every other extensively used method of analyzing marketplace sentiment is sentiment evaluation, which explains to us how stock price volatility reacts to human beings's sentiment In economic evaluation, our examine highlights the significance of predictive modeling and sensitivity analysis through showing that they are correlated. Using this fusion, we can make better predictions in the stock market, helping participants make informed decisions based on well-analyzed data. © 2024 IEEE.

关键词： Financial Analytics machine learning Natural Language Processing Sentiment Analysis Stock Market Prediction

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FIGCONs: Exploiting FIne-Grained CONstructs of Facial Expressions for Efficient and Accurate Estimation of In-Vehicle Drivers’ Statistics 5th

FIGCONs: Exploiting FIne-Grained CONstructs of Facial Expre...

引用

5th international conference on HCI in Mobility, Transport, and Automotive Systems, MobiTAS 2023, held as part of the 25th international conference on human-computer interaction, HCII 2023

作者： Bi, Zhuoran Ming, Xiaoxing Liu, Junyu Peng, Xiangjun Jin, Wangkai User-Centric Computing Group Hangzhou China

ISBN: (纸本)9783031359071

Recent advances in-vehicle monitoring of drivers’ statistics attempt to leverage machine-learning (ML)-based techniques for prediction, credited to its high accuracy and less cost for deploying redundant sensors/cameras inside the vehicle. However, existing approaches for in-vehicle ML-based predictors heavily rely on the raw driver data for processing, which mcan push the burdens on multiple components, ranging from on-vehicle devices to Internet-of-Vehicles (e.g., on-vehicle computation and storage units). To obtain a better balance between the performance and cost, we propose FIne-Grained CONstructs (FIGCONs) of collected drivers’ statistics, to further exploit its applicability. We do so by integrating it with state-of-the-art ML-based approaches to predict in-vehicle statistics. the experimental results deliver three key findings, which showcase the benefits of FIGCONs and the potentials for practical deployments of in-vehicle ML-based predictors. We hope our work can stimulate more future works and potential practices in similar manners. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： human computer interaction

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Artificial Intelligence and machine learning in Autism Detection: From Common to Rare Disorders 5th

Artificial Intelligence and Machine Learning in Autism Detec...

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5th international conference on Computing, Communications, and Cyber Security, IC4S 2023

作者： Gupta, Arshia Malhotra, Deepti Department of Computer Science and Information Technology Central University of Jammu J&K Samba181143 India

ISBN: (纸本)9789819725496

Autism is an attitude-driven issue that generally resulted in negative stigma toward anyone. Autism is related to a developmental disability that is caused because of differences in the brain. People with autism problem generally have social communication and interaction problems. It is not always that people having autism issue experience developmental disability;however, this issue can also originate from other diseases as well. Autism disorder is categorized into common and rare diseases. Under common autism disorder, autism spectrum and attention deficit hyperactivity disorder. Research has been conducted to detect and correct the issues associated with common autism disorders. AI and machine-learning-based mechanisms are available for the detection of common autism-related diseases. For rare, autism-related diseases, limited work has been done. For rare autism including Rett syndrome and childhood disintegrative disorder can be screened with tests. Rare AD screening tests however are time- and money-consuming. Techniques including machine learning are researched over with the help of which detection of ASD and ADHD and decide whether to get a clinical test for the same or not based on generated predictions. AD is not curable disease however technology-driven methodology can be used for early detection and avoiding additional harm to the body. In modern era, artificial intelligence and machine-learning-based mechanism have been evolved to complement the traditional clinical tests for the diagnosis of AD. the focus of this paper is to explore the AI and machine-learning-based mechanisms along with clinical tests that are used for detection of AD. Furthermore, this paper finds out the most significant mechanism used for the classification of AD-based disease. Rare disease however yet not explored with technology and hence open issues and challenges associated with rare diseases has been explored through this research. © the Author(s), under exclusive license to Sp

关键词： Diseases

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

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

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Intelligent Computing and human-computer interaction (ICHCI), international conference on

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

ISBN: (数字)9798350368284

ISBN: (纸本)9798350368291

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 $\mathrm{R}^learning$ value was found to be 0.9734 while RMSE stood at 0.1267 $\text{kJ}/(\text{kg}\cdot \mathrm{K})$ .

关键词： Heating systems machine learning algorithms Liquids Computational modeling machine learning Predictive models Prediction algorithms Hafnium oxide Mathematical models Refrigerants

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No Prompting Frozen Foundation Models: Interactive Medical Volume Segmentation using Continual Test Time Adaptation of Compact Models 24

No Prompting Frozen Foundation Models: Interactive Medical V...

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15th Indian conference on computer Vision Graphics and Image Processing

作者： Borkar, Kushal Reen, Abhilaksh Singh Jawahar, C. V. Arora, Chetan Int Inst Informat Technol Hyderabad Hyderabad India Indian Inst Technol Delhi Delhi India IIIT Hyderabad Hyderabad India

ISBN: (纸本)9798400710759

Automated segmentation of medical image volumes promises to reduce costly medical experts' time for annotation. However, using machine learning for the task is challenging due to variations in imaging modalities and scarcity of patient data. While interactive image segmentation methods and foundational models incorporating user-provided prompts to refine segmentation masks have shown promise, they overlook crucial sequential information between the slices in 3D medical image volumes and videos, resulting in discontinuities in the segmentation results. this paper proposes a new framework that dynamically updates model parameters during inference in a test time training framework using user-provided scribbles. Our framework preserves acquired knowledge from the previous slices of the current medical volume and the training dataset via student-teacher learning. We evaluate our method on diverse CT, MRI, and microscopic cell datasets. Our framework significantly reduces user annotation time by a factor of 6.72x. Compared to other interactive segmentation methods, we reduce the time by a factor of 2.64x. Our method also outperforms prompting foundation models for segmentation by achieving a dice score of 0.9 in 3-4 interactions compared to 5-8 user interactions for the foundation model, significantly reducing annotation time for the CT and MRI volumes.

关键词： Medical Image Analysis Image Segmentation human-computer interaction Continual learning

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Automatic Facial Emotion Recognition: A Review 5

Automatic Facial Emotion Recognition: A Review

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5th College of Science international conference on Recent Trends in Information Technology, CSCTIT 2022

作者： Ali, Shahad Salh Al'Ameri, JamilaH. Abbas, thekra Mustansiriyah University Baghdad Iraq Mustansiriyah University College of Science Computer Science Dept. Baghdad Iraq

ISBN: (纸本)9798350334920

Facial Expression Recognition(FER) is one of the most popular research area in human machine interaction and computer vision based applications. In recent years, it is attracting wide spread researchers' interest in several fields. there markable success of technologies and the development of hardware sensors motivate researchers to develop FER systems in vast application areas and several review papers are available. the existing FER researches limited by learning only six based emotions. It conflicts with our every day life, emotions are more complex. Researchers have to build larger datasets in the future in order to create powerful architectures that recognize not only the basic emotions but also the secondary ones. In this review, we briefly introduce the benchmark datasets related to FER systems and divide the proposed methods in the last three years into three main groups: Deep learning(DL), machine learning(ML) and hybrid architectures in order to make improvements in this field. © 2022 IEEE.

关键词： Emotion Recognition

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Empirical study of emotional state recognition using multimodal fusion framework 23

Empirical study of emotional state recognition using multimo...

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5th international conference on Image, Video and Signal Processing, IVSP 2023

作者： Ruchilekha Singh, Manoj Kumar DST- Centre for Interdisciplinary Mathematical Sciences Banaras Hindu University Varanasi221005 India Department of Computer Science Institute of Science Banaras Hindu University Varanasi India

ISBN: (纸本)9781450398381

Recent trends towards emotion recognition are not only limited to text, audio and visual modalities;instead, physiological signals are tempting attention of many researchers in challenging field of human-computer interaction. In this paper, an empirical analysis is conducted with static patterns of physiological signals over time for human emotional state using machine learning approach. It is also investigated in this study that which brain lobe is most efficient to emotions. Here, subjective ratings are transformed to 3-dimensional VAD space which are then grouped into five discrete emotion labels using three clustering techniques via k-means, k-medoids and fuzzy c-means. Various features are extracted from EEG and other peripheral signals separately and are validated with the labels obtained from these clustering mechanisms using traditional classification algorithms. the aim of this study is to evaluate the performance of EEG signals and peripheral signals individually and analyze the results when these modalities are fused together. It is observed that the results obtained with multimodal fusion acquires the highest accuracy of 91.90% with 0.98 AUC score for ensemble subspace-KNN classifier when validated using clustered labels of fuzzy c-means via city-block metric. the study is conducted on DEAP dataset using 32 subjects and 15 named emotions only. It is also observed that temporal regions of brain are mostly correlated to emotions. Hence, these temporal EEG channels can be utilized for human emotion recognition towards human-computer interaction. © 2023 ACM.

关键词： Emotion Recognition

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Predicting human Brain States with Transformer

Predicting Human Brain States with Transformer

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Workshop on Longitudinal Disease Tracking and Modeling with Medical Images and Data, LDTM 2024, 5th international Workshop on Multiscale Multimodal Medical Imaging, MMMI 2024, 1st Workshop on machine learning for Multimodal/-sensor Healthcare Data, ML4MHD2024 and Workshop on Multimodal learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th international conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2024

作者： Sun, Yifei Cabezas, Mariano Lee, Jiah Wang, Chenyu Zhang, Wei Calamante, Fernando Lv, Jinglei School of Biomedical Engineering University of Sydney SydneyNSW2008 Australia Brain and Mind Center University of Sydney SydneyNSW2050 Australia Central Clinical School University of Sydney SydneyNSW2050 Australia Macquarie University Hearing Macquarie University SydneyNSW2109 Australia Sydney Neuroimaging Analysis Centre University of Sydney SydneyNSW2050 Australia School of Computer and Cyber Sciences Augusta University AugustaGA30901 United States Sydney Imaging University of Sydney SydneyNSW2006 Australia

ISBN: (纸本)9783031845246

the human brain is a complex and highly dynamic system, and our current knowledge of its functional mechanism is still very limited. Fortunately, with functional magnetic resonance imaging (fMRI), we can observe blood oxygen level-dependent (BOLD) changes, reflecting neural activity, to infer brain states and dynamics. In this paper, we ask the question of whether the brain states represented by the regional brain fMRI can be predicted. Due to the success of self-attention and the transformer architecture in sequential auto-regression problems (e.g., language modelling or music generation), we explore the possibility of the use of transformers to predict human brain resting states based on the large-scale high-quality fMRI data from the human connectome project (HCP). Current results have shown that our model can accurately predict the brain states up to 5.04 s with the previous 21.6 s. Furthermore, even though the prediction error accumulates for the prediction of a longer time period, the generated fMRI brain states reflect the architecture of functional connectome. these promising initial results demonstrate the possibility of developing generative models for fMRI data using self-attention that learns the functional organization of the human brain. Our code is available at: https://***/syf0122/brain_state_pred. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Brain mapping

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human Pose Transfer with Depth Information

Human Pose Transfer with Depth Information

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machine learning and computer Application (ICMLCA), international conference on

作者： Yuhan Song Pengyou Pan Peng Pan Shanghai Foreign Language School Affiliated to SISU Hangzhou Hangzhou P.R. China

ISBN: (数字)9798331530334

ISBN: (纸本)9798331530341

human pose transfer refers to synthesizing an image of a target person with the pose of a source person. Many current methods have achieved good results. However, the images generated on a two-dimensional plane will cause confusion in the positions of different body parts due to the lack of depth information in three dimensions, which limits the visual quality of the results. this paper introduces monocular depth estimation to solve this problem and proposes a new framework that can predict the depth information of the human body and thus synthesize high-quality target human appearance.

关键词： Visualization machine learning computer applications

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