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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171-180 订阅

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Analyzing Deep learning in IoT Platform 5th

Analyzing Deep Learning in IoT Platform

引用

5th international conference on Advances in Information Communication Technology and Computing, AICTC 2024

作者： Venugopal, Anita Goar, Vishal Kumar Deptartment of IT Unit Dhofar University Salalah Oman Engineering College Bikaner Bikaner India

ISBN: (纸本)9789819761050

Internet of things (IoT), a modern technology, is a network of devices with sensors and embedded processors which can work even without the use of internet. It can digitally control the real world wired or wirelessly. It receives attention as IoT-enabled products make human more intelligent and can be involved in different aspects of our lives. Continuous development of IoT domain and large number of devices connected to internet is one of the challenges faced by this technology. Many modern approaches such as computer vision and machine learning have been devised to cope up to meet the challenges. In this work, basic IoT elements are discussed and provide a review of modern addressing method: deep learning (DL) in IoT platform. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Multi-person Collaborative Switch machine Simulation Training System Based on Augmented Reality 24

Multi-person Collaborative Switch Machine Simulation Trainin...

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16th international conference on computer Modeling and Simulation, ICCMS 2024

作者： Zhao, Yuanhang Su, Hu Ma, Chi Han, Shibo School of Electrical Engineering Southwest Jiaotong University Chengdu China

ISBN: (纸本)9798400717215

In view of the problems that current railway training cannot meet the on-site operation and learning of trainees, and the training efficiency is low, a multi-person collaborative switch machine simulation training system based on augmented reality was designed. On the HoloLens 2 platform, the scene model is repositioned by detecting and identifying QR codes. the network communication architecture is designed based on the needs of multi-person collaborative training. When interacting with the model, an interlocking mechanism is designed to solve the problem of user misoperation. Finally, Unreal Engine 5 is used to implement a multi-person collaborative simulation training system. Practice has proved that the scene model relocation accuracy of this system is good, the picture is clear and smooth, the interactivity is good, and the training effect is good. © 2024 Copyright held by the owner/author(s).

关键词： human computer interaction

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Fifth international conference on computer Vision and Information Technology, CVIT 2024

Fifth International Conference on Computer Vision and Inform...

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5th international conference on computer Vision and Information Technology, CVIT 2024

ISBN: (纸本)9781510686755

the proceedings contain 10 papers. the topics discussed include: texture-attention discriminator for real-world image super-resolution;two-stage video scene segmentation method based on multimodal semantic interaction;design of material sorting system based on OpenMV;research on intelligent estimation of human posture in multiplayer complex scenarios;a video retrieval algorithm with embedded position coding for moment localization and highlight detection;a bio-inspired model for object motion direction and speed detection against colored backgrounds;analyzing handgun handling states: a deep learning approach using YOLOv8;using hash learning for a multi-model fashion recommendation system;and research on auditing of large-scale instruments and equipment opening and sharing in universities based on data mining.

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Design of human Resource Optimal Allocation Model for Electric Power Construction Enterprises Based on machine learning 5th

Design of Human Resource Optimal Allocation Model for Electr...

引用

5th international conference on Big Data and Security (ICBDS)

作者： Li, Shiyue Xiao, Yating Yuan, Weichen State Grid Informat & Telecommun Beijing Branch Beijing 100052 Peoples R China

ISBN: (纸本)9789819743896;9789819743902

Electric power construction enterprises have the characteristics of huge capital occupation, long project cycle and complicated management interface. Although the project management theory has been widely used in the construction of electric power enterprises in my country, there are still many problems. human resource planning, as an important part of enterprise human resource management, is the extension of enterprise strategy in human resource management. In order to optimize the resource management process, reduce business workload, and improve work efficiency, the introduction of machine learning methods to realize the functions of human resource management system can reduce business workload, thereby improving work efficiency and system performance [1]. the model is designed and implemented by applying machine learning technology to human resource management system. Build a human resource demand model system that can control the total amount of labor, labor allocation, optimize the workforce, and standardize labor strategies, and find out the corresponding relationship and law between the development of the power grid enterprise workforce and the scale of business growth, production efficiency and economic benefits. Realize the forecast of the quantity, structure and quality of human resource demand for the strategic development of the enterprise, and formulate an effective recruitment strategy in combination with the supply of internal and external talent markets to optimize the allocation of human resources. For this purpose, it can accurately predict human resource needs, provide decision support for enterprise human resource development planning, provide reliable human resource guarantee for the realization of enterprise strategic goals, and lay a solid human resource foundation for the sustainable and healthy development of the enterprise.

关键词： machine learning electric power construction enterprise human resource optimization model

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Proceedings - 2023 IEEE 5th international conference on Cognitive machine Intelligence, CogMI 2023

Proceedings - 2023 IEEE 5th International Conference on Cogn...

引用

5th IEEE international conference on Cognitive machine Intelligence, CogMI 2023

ISBN: (纸本)9798350323832

the proceedings contain 27 papers. the topics discussed include: comparative study of causal discovery methods for cyclic models with hidden confounders;location-adaptive generative graph augmentation for fraud detection;forecasting the spread of toxicity on twitter;Prompt-to-OS (P2OS): revolutionizing operating systems and human-computer interaction with integrated ai generative models;skills extraction from entities in Arabic CVs;the intersection of usability evaluation and machine learning in software systems;amplifying object tracking performance on edge devices;active region-based flare forecasting with sliding window multivariate time series forest classifiers;and a comparative study of model-agnostic and importance-based feature selection approaches.

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5th international conference on Deep learning theory and Applications, DeLTA 2024

5th International Conference on Deep Learning Theory and App...

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5th international conference on Deep learning theory and Applications, DeLTA 2024

ISBN: (纸本)9783031667046

the proceedings contain 45 papers. the special focus in this conference is on Deep learning theory and Applications. the topics include: CNN-N-BEATS: Novel Hybrid Model for Time-Series Forecasting;development and Applications of Gesture-Controlled Drones: Advances in Hand Gesture Recognition for Aerial Navigation;towards Natural-Sounding Speech to Text in English;scoping Review of Active learning Strategies and their Evaluation Environments for Entity Recognition Tasks;solar Activity Impact on Firefighter Interventions: Factors Analysis;Detecting Flow via a machine learning Model in a MOOC Context;Online Job Posting Authenticity Prediction with machine and Deep learning: Performance Comparison Between N-Gram and TF-IDF;Evolving Deep Architectures: A New Blend of CNNs and Transformers Without Pre-training Dependencies;Closing the Sim-to-Real Gap: Enhancing Autonomous Precision Landing of UAVs with Detection-Informed Deep Reinforcement learning;Mitigating Class Imbalance in Healthcare AI Image Classification: Evaluating the Efficacy of Existing Generative Adversarial Networks;more than Noise: Assessing the Viscosity of Food Products Based on Sound Emission;vector Analysis of Deep Neural Network Training Process;secure Coalition Formation for Federated machine learning;action Conditioned Attention Encoder-Decoder and Discriminator for human Motion Generation;citation Polarity Identification in Scientific Research Articles Using Deep learning Methods;exploring Physiology-Based Classification of Flow During Musical Improvisation in Mixed Reality;refining Weights for Enhanced Object Similarity in Multi-perspective 6Dof Pose Estimation and 3D Object Detection;end-to-End Video Surveillance Framework for Anomaly Detection and Person Re-identification;few-Shot learning with Novelty Detection.

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Exploring Physiology-Based Classification of Flow During Musical Improvisation in Mixed Reality 1

引用

5th international conference on Deep learning theory and Applications (DeLTA)

作者： Schlagowski, Ruben Mertes, Silvan Schiller, Dominik Can, Yekta Said Andre, Elisabeth Univ Augsburg Chair Human Ctr Artificial Intelligence Augsburg Germany

ISBN: (数字)9783031666940

ISBN: (纸本)9783031666933;9783031666940

the flow state is desirable in many activities, e.g., while making music or being active in Virtual, Augmented, and Mixed Realities. With the long-term goal of creating affective systems that can consider the user's flow state in real-time, we evaluated an approach for real-time flow classification during networked music performance using a deep neural network. We trained our classifier based on physiological signals (PPG and GSR) that we recorded and annotated in a laboratory study, including jamming musicians. the results that we present in this paper confirm the technical validity of this approach while also facing challenges that stem from inter-rater reliability and a heavily unbalanced dataset.

关键词： Affective computing machine learning Physiological signals Flow state Music collaboration Networked music performance Mixed reality Augmented reality Virtual reality

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Optimized Hindi Negation Detection Using a Hybrid Rule-Based and BERT Model 5

Optimized Hindi Negation Detection Using a Hybrid Rule-Based...

引用

5th international conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Shah, Nirja Pareek, Jyoti Gujarat University Department of Computer Science India

ISBN: (纸本)9798331518097

A significant aspect of language is the ability to negate, which is also an essential part of the process of extracting information from written text. the fields of finance, healthcare, and education all place a significant amount of weight on this particular subtask. throughout the years, a variety of strategies have been analyzed in order to find a solution to this issue. Negation Detection and Scope Resolution is crucial for Sentiment Analysis and Chatbots, as it affects the meaning and relevance of text. Much of the research on this problem has focused on medical data and financial data where negation cues are prevalent (e.g., medical reports). Negation is easy for people to recognize, but computer systems struggle to identify the phrases that signal it and define its extent. Early systems employed rules and heuristics to handle the problem of discovering negation cues and their contexts. However, human language is complicated, therefore these methods were inaccurate. After exploring several strategies, Deep learning-based approaches emerged as promising. CNNs, particularly with BERT and, more recently, BiLSTMs, rule-based systems, machine learning classifiers, and conditional random field models are all examples of such techniques. the purpose of this review paper is to discuss the topic of negation detection in Hindi through the use of natural language processing, specifically the BERT approach and the hybrid rule-based approach. Additionally, a literature survey of over 15 papers will be discussed, which will include objectives, the research contribution, as well as its limitations and future work. Additionally, the importance of negation detection in hind language will be discussed, as well as how the BERT model is useful for negation detection and the challenges that should be addressed. © 2024 IEEE.

关键词： Deep learning

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Investigating Windowing Techniques in Emotion Classification with ECG and machine learning 5

Investigating Windowing Techniques in Emotion Classification...

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5th IEEE international conference on Cybernetics, Cognition and machine learning Applications, ICCCMLA 2023

作者： Kumar Govarthan, Praveen Kumar, Sriram Ganapathy, Nagarajan Agastinose Ronickom, Jac Fredo Varanasi India Indian Institute of Technology Department of Biomedical Engineering Kandi Hyderabad India

ISBN: (纸本)9798350338287

Automated emotion recognition using physiological signals, particularly Electrocardiogram (ECG), has diverse applications in human-computer interaction, healthcare, and psychology. this study proposes a novel ECG-based emotion recognition approach, utilizing time-series to image encoding, texture-based features, and machine-learning algorithms. the Continuously Annotated Signals of Emotion dataset is used, and emotional states are categorized based on arousal and valence annotations. ECGs are segmented into 5 and 7-window segments and transformed into 2D representations using Markov Transition Field (MTF). Extracting 43 features from the Gray-Level Co-occurrence Matrix and Gray-Level Run Length Matrix (GLRLM), three classifiers, including Random Forest (RF), Support Vector machine, and eXtreme Gradient Boosting (XGB), are employed for emotion classification. the 7-window approach yields superior results, achieving a peak accuracy of 76.69% with XGB. High-Valence Low-Arousal emotional states are recognized best, with the highest F-measure of 61.9%. the findings suggest the potential for accurate and efficient emotion recognition using ECG, MTF, and machine-learning classifiers. © 2023 IEEE.

关键词： Electrocardiograms

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Reimagine Sentiments in Nature Language through Crystalize- Water Visualization 5

Reimagine Sentiments in Nature Language through Crystalize- ...

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5th international conference on Artificial Intelligence and Data Sciences, AiDAS 2024

作者： Png, Wen-Hao Chang, Fang-Yu Lo, Jie-Jin Hsu, Su-Chu Faculty of Information and Computer Technology Universiti Tunku Abdul Rahman Kampar Malaysia Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan Institute of Information Security National Tsing Hua University Hsinchu Taiwan Research Center for Technology and Art National Tsing Hua University Hsinchu Taiwan

ISBN: (纸本)9798331528553

Text art translates words into artworks through various mediums, including neon signage, light art, and hand- painted or drawn text. the visual representations focus on the typographical design and the ideas and meaning conveyed through human language. While sentiment analysis is a pivotal area of study in natural language processing (NLP) that aims to determine the sentiment expressed in text data with wide range of applications span various domains, including marketing, social media monitoring, and opinion mining. In this work, we reimagine the sentiments in nature language for artworks through a creative tandem composition of machine learning, physics, and art. By reframing the textual sentiment as virtual snow crystal fabrication, we created charming yet representative visualizations based on wide variety of cyber and literature texts, including classical poems and social network comments. the visualization aims to creatively generate text-based art and raise awareness of how sentiment in natural language can affect human emotion. © 2024 IEEE.

关键词： Sentiment analysis

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