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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31-40 订阅

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Enhancing Brain Tumor Diagnosis with MedMamba: A Novel Application in Neural Imaging Analysis 5

Enhancing Brain Tumor Diagnosis with MedMamba: A Novel Appli...

引用

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

作者： Chen, Xinrui School of Computer Science and Technology Beijing Jiaotong University Beijing China

ISBN: (纸本)9798350368284

Brain tumors pose a great threat to human health and need to be diagnosed at an early stage. MRI plays an important role in assisting diagnosis due to its clear soft tissue visualization. Currently, the application of deep learning techniques in medical imaging shows the possibility of outperforming humans in terms of diagnostic accuracy and speed. this study explores the application of transfer learning and MedMamba models for brain tumor diagnosis, with a particular focus on their role in performing MRI brain tumor image classification. the MedMamba model is introduced and evaluated, which is a new adaptation of the Mamba framework optimized for the medical imaging field. this study not only compares the performance of MedMamba models with traditional deep learning models such as Convolutional Neural Network and Vision Transformer, but also highlights the effectiveness of transfer learning in improving these models for specialized tasks in medical diagnosis. the main purpose is to describe the performance optimization capabilities provided by transfer learning and to demonstrate the inherent advantages of the MedMamba model. the model combines an innovative design with a streamlined architecture for efficient image processing. the positive role of transfer learning in the task of brain tumor image classification and the potential of MedMamba to set a new benchmark in this area are highlighted through comprehensive testing and evaluation of results on a brain MRI image dataset. © 2024 IEEE.

关键词： Convolutional neural networks

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Stock Market Prediction Using machine learning 5

Stock Market Prediction Using Machine Learning

引用

5th IEEE international conference for Emerging Technology, INCET 2024

作者： Math, Bhavana Bevinakatti, Om Hosamani, Sheetal Patil, Priyadarshini School of Computer Science KLE Technological University Karnataka Hubballi India

ISBN: (纸本)9798350361155

the inherent volatility of the financial market has long posed a challenge when it comes to investing in a portfolio of assets. Basic models lack the ability to precisely predict future asset prices. machine learning, the discipline of enabling computers to perform tasks that often require human intelligence, is currently the dominant trend in scientific research. the aim of this essay is to create a model using a machine learning method. the main objective of this research is to evaluate the degree of precision at which a machine learning algorithm can provide predictions. Predicting stock market prices is a complex task that typically involves significant collaboration between humans and computers. the network is trained and evaluated to assess its accuracy using datasets of varying sizes, and the results are documented in a tabular style. the aim of this study is to predict stock market prices with the intention of making more knowledgeable and precise financial decisions. © 2024 IEEE.

关键词： Financial markets

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Task Separation and Knowledge Sharing for Class Incremental learning 5

Task Separation and Knowledge Sharing for Class Incremental ...

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

作者： Zhang, Jiali Qiao, Xiaoyan School of Computer Science and Technology Shandong Technology and Business University Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Immersion Technology and Evaluation Shandong Engineering Research Center Shandong Yantai China School of Mathematics and Information Science Shandong Technology and Business University Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Immersion Technology and Evaluation Shandong Engineering Research Center Shandong Yantai China

ISBN: (纸本)9798350368284

Methods based on dynamically expanding architectures can effectively mitigate catastrophic forgetting in class incremental learning (CIL), but they often overlook information sharing and integration between subnetworks, leading to performance degradation and increased parameter count and memory consumption as the network expands. To address these complex issues, we introduce a new method called Task Separation and Knowledge Sharing (TSKS), which transfers knowledge at both the logical and feature levels through Hybrid Knowledge Distillation (HKD). In logical distillation, we combine Global Knowledge Distillation (GKD) and Task Knowledge Distillation (TKD) to establish connections between tasks. To prevent the model from favoring new classes, we design an Attention- Based Feature Fusion (ABFF) mechanism to capture important features. Additionally, we introduce a Soft-Margin Auxiliary Loss (SMAL) to help the model learn new concepts. Extensive experiments show that TSKS outperforms existing models on CIFAR100 and ImageNet100, regardless of whether memory samples are used. Notably, TSKS demonstrates superior performance in memory-free settings, indicating a reduced reliance on rehearsal memory. © 2024 IEEE.

关键词： Adversarial machine learning

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Prediction of Vehicle Carbon Emission Using machine learning 5

Prediction of Vehicle Carbon Emission Using Machine Learning

引用

5th international conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Ashok, K. Rithishbrahma, Palem New Horizon College of Engineering Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350379945

the automotive industry is a major contributor to carbon emissions, posing a threat to the environment and human health. this research explores the potential of machine learning for predicting vehicle CO2 emissions. It utilizes a comprehensive dataset encompassing diverse factors like vehicle type, engine specifications, driving patterns, and environmental conditions. Feature engineering and model training with various algorithms like linear regression, decision trees, and neural networks are employed. the findings demonstrate the effectiveness of machine learning in predicting emissions with high accuracy, offering valuable insights into influencing factors. the developed models empower the automotive industry to manage emissions more efficiently, contributing to climate change mitigation and a more sustainable future. this study highlights the potential of machine learning in addressing critical environmental challenges. © 2024 IEEE.

关键词： Zero-carbon

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Gesture-Driven Virtual Mouse: Empowering Accessibility through Hand Movements 5

Gesture-Driven Virtual Mouse: Empowering Accessibility throu...

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

作者： Julian, Anitha Suresh, Danush Ramyadevi, R. Saveetha Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350383522

the mouse has been a vital tool for human-computer interaction, with wired, wireless, and Bluetooth variations requiring power to connect a dongle to a PC. the proposed work uses the latest technology in computer vision and machine learning to recognize hand movements without needing extra equipment. Compatible with CNN models through mediapipe implementation, it enables various actions via various hand movements, all on a single computer. Utilizing a camera as the input device, the system relies on Python and OpenCV. the camera's output is displayed on the system's screen, allowing users to fine-tune their interactions. © 2024 IEEE.

关键词： computer vision gesture recognition hand gestures interaction Virtual mouse

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English Language Translation machine System Based on human-computer interaction 5th

English Language Translation Machine System Based on Human-C...

引用

5th international conference on Real Time Intelligent Systems, RTIS 2023

作者： Tang, Liefu Shu, Ting Hefei Technology College Anhui Hefei China

ISBN: (纸本)9783031558474

this paper mainly studies an English language translation machine system based on a human-computer interaction enhanced algorithm. the system combines artificial intelligence technology with human-computer interaction design to improve the accuracy of English language translation and enhance the user experience. Firstly, the existing technologies and algorithms for English language translation are researched, and their problems and limitations are analyzed. then, a novel translation machine system framework based on reinforcement algorithms is proposed. this framework integrates reinforcement learning and natural language processing technology to enhance translation accuracy and adaptability through training and optimizing algorithm models. Next, the application of human-computer interaction design in the translation machine system is studied. through user surveys and needs analysis, a concise and intuitive user interface and interaction methods are designed to improve the user’s convenience and experience. Finally, system experiments and evaluations are conducted to validate the advantages of the English language translation machine system based on a human-computer interaction enhanced algorithm regarding translation accuracy and user satisfaction. the experimental results show that compared to traditional translation machine systems, this system has higher accuracy and provides a better user experience. this research has theoretical and practical significance for improving and developing English language translation machine systems. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： human computer interaction

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Cardiovascular Disease Prediction Using machine learning and Deep learning Algorithms 5

Cardiovascular Disease Prediction Using Machine Learning and...

引用

5th international conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Murugesan, M. Mohamednowfal, S. Dineshwaran, S. Ashok Kumar, C. M.Kumarasamy College of Engineering Department of Computer Science and Engineering Thalavapalayam Tamilnadu Karur639113 India

ISBN: (纸本)9798350385649

Heart is the human body's most vital organ. As a result, heart disease is considered as a serious condition, which may carry a significant long-term risk of death. However, the existing approaches cannot efficiently identify inherent correlations and patterns from e-health data. To save lives, a detailed data analysis process is required. Incorporating such analysis is difficult but it is highly essential to implement disease detection. this study suggested using machine learning to perform health analytics, which would allow for more accurate patient data analysis. Here, data mining techniques can be used to develop an intelligent model based on patient risk factor datasets. this study analyses how deep learning and machine learning approaches can be used to diagnose heart diseases. Recently, various data mining classifiers are developed to accurately and effectively identify heart diseases. MLP, a complex classification algorithm, employs artificial neural networks with deep learning capabilities. To generate accurate findings, the proposed method makes use of deep learning and data mining techniques. © 2024 IEEE.

关键词： Data Analytics

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Active learning in Industrial Defect Detection: A Dynamic Approach to Uncertainty and Representativeness 5

Active Learning in Industrial Defect Detection: A Dynamic Ap...

引用

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

作者： Shen, Yuhao Liu, Feifei Zhang, Zhen School of Mechanical and Electrical Engineering Jiangxi University of Science and Technology Ganzhou China School of Electrical Engineering and Automation Jiangxi University of Science and Technology Ganzhou China

ISBN: (纸本)9798350368284

the storage and annotation of industrial data is a costly and time-intensive work in developing a defects detector. To overcome this challenge, this study proposes an effective annotation workflow and an active learning strategy that integrates uncertainty and representativeness to construct a reliable defects detection model. the annotation workflow involves both the advisor and the labeling team for the inspection of industrial data that depends on specialized knowledge in a particular area. the active learning strategy combines the uncertainty-based and the representativeness-based sampling metrics flexibly to assess the informativeness of the unlabeled data. Besides, a training strategy for active learning models is used to address not only data uncertainty but also the generalization of feature distribution across all data. We benchmark the proposed method on Bottled Baijiu Defect Detection Dataset, utilizing RetinaNet as the baseline detector. Results demonstrate that the proposed method significantly reduces labeling costs and data storage requirements, which is crucial for industrial defect detection. © 2024 IEEE.

关键词： Active learning

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Real-Time Gesture Recognition for Volume Control Using Python and ML Techniques 5

Real-Time Gesture Recognition for Volume Control Using Pytho...

引用

5th international conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Musthafa, Syed Murugan, Lavanya Mutharasu, M. Vadivel, T. Fathima, H. Leelavathi, M. M.Kumarasamy College of Engineering Department of Information Technology Tamil Nadu Karur639 113 India American International University Computer Science Kuwait Andhra Pradesh Madanapalle517325 India KSR College of Arts and Science Department of Computer Applications Tamil Nadu Tiruchengode637 215 India Sree Saraswathi Thyagaraja College Department of AI & ML Tamil Nadu Pollachi642205 India

ISBN: (纸本)9798331530013

this research study proposes a gesture-based volume control system that combines computer vision and machine learning to improve user interaction with audio devices. By applying the OpenCV library for real-time video processing and MediaPipe's hand-tracking module, the system interprets hand gestures to control the volume. this system measures the distance between the index finger and thumb. Using this distance, the system adjusts the system's audio volume by using the Pycaw library. the purpose is to have a touchless interface wherein the user can control audio levels by simple hand movements to enhance accessibility and usability across various environments. this is an promising alternative to traditional input methods as the demand for more natural human-computer interaction grows, this gesture-based approach represents a leveraging computer vision and machine learning technique, which accurately detects gestures in volume control. the goal of this project is to design a touchless volume control system that relies on gesture input, computer vision, and machine learning techniques in order to bring an intuitive user experience. the proposed system integrates OpenCV for real-time video processing and makes use of Media Pipe's advanced hand-tracking abilities to identify gestures, which are the distance between the thumb and index finger, to control the volume of audio. Pycaw is used to interact with the audio output of the system to dynamically control the volume. this gesture-based interface is designed to improve accessibility, usability, and user experience by providing a modern, intuitive alternative to traditional volume control methods. © 2024 IEEE.

关键词： Gesture recognition

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Meta-Heuristic Hyperparametric Optimization Model BO-GCN-LSTM for Spatial interaction-Oriented Prediction Based on Deep learning 5

Meta-Heuristic Hyperparametric Optimization Model BO-GCN-LST...

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

作者： Cao, Xiaojing College of Geodesy and Geomatics Shandong University of Science and Technology Qingdao211590 China

ISBN: (纸本)9798350368284

Understanding spatial interactions is crucial for analyzing human activities across regions, yet predicting these interactions between cities on a large scale remains challenging due to the complexity of influencing factors. this study integrates a meta-heuristic optimization algorithm with a deep learning model to enhance the accuracy of such predictions nationwide. A Graph Convolutional Network (GCN) is employed to aggregate geographic features and capture spatial dependencies, while a Long Short-Term Memory Network (LSTM) identifies temporal patterns. To optimize the models, a Bayesian optimization algorithm (BO) and Genetic Algorithm (GA) were utilized to search for optimal hyperparameters, resulting in the BO-GCN-LSTM and GA-GCN-LSTM models. Comparative analysis reveals that the MSE values of the BO-GCN-LSTM and GA-GCN-LSTM models are 23% and 22.4% lower, the RMSE values are 23.9% and 15.7% lower, and the R2 values are 18% and 20% higher, respectively, than those of the GCN-LSTM model. these results demonstrate that the BO-GCN-LSTM model is particularly well-suited for large-scale predictions of spatial interactions between cities. © 2024 IEEE.

关键词： Prediction models

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