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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 559 条记录，以下是251-260 订阅

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Deep learning-Based Detection of Parkinson’s Disease Using Gait 5th

Deep Learning-Based Detection of Parkinson’s Disease Using...

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5th international conference on Advances in Distributed Computing and machine learning, ICADCML 2024

作者： Kumar, Aman Jaiswal, Akanksha Sharma, Lakhan Dev Singh, Priyanka Kumar, Jayendra Electronics and Communication Engineering NIT Hamirpur Himachal Pradesh Hamirpur177005 India School of Electronics Engineering VIT-AP University Inavolu Andhra Pradesh Amaravathi522237 India School of Computer Science and Engineering VIT-AP University Andhra Pradesh 522237 India

ISBN: (纸本)9789819718405

Parkinson’s disease (PD) affects over a dozen million people worldwide. there is presently no solution for this, much to the dismay of these sufferers. this needs early detection for enhancing the patient’s overall well-being. the goal of our research is to create a prototype tool that can detect PD symptoms and assess their severity rate (dependent on the UPDRS) using gait signals. the projected model beats state-of-the-art approaches accomplishing an accuracy of 98.7% owing to its ability to extract relevant gait characteristics from multiple input signals. We fostered 1D-Convnets that automatically extracts essential deep features for effective gait classification, eliminating the need for human feature extraction. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning

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Enhanced Traffic Management in IoT-Integrated Internet of Vehicles (IoV) with Optimized Routing and Deep learning Algorithm 5th

Enhanced Traffic Management in IoT-Integrated Internet of Ve...

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5th international conference on Advances in Distributed Computing and machine learning, ICADCML 2024

作者： Sahoo, Arundhati Tripathy, Asis Kumar School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9789819735228

the integration of the Internet of things (IoT) into vehicular networks has paved the way for the exciting development of the Internet of Vehicles (IoV) within Intelligent Transportation Systems (ITS). these breakthroughs bring about enhancements in urban transportation services, including improved quality and interaction, reduced costs, more efficient resource utilization, and enhanced traffic management capabilities. the incorporation of IoV into transportation has the potential to address various traffic-related challenges in smart cities. Nevertheless, optimizing routing and monitoring network traffic presents significant challenges, primarily due to limited node-to-node communication capabilities. To tackle these problems, this study proposes the BiLSTM-GreedyNet, which combines the Improved Greedy Perimeter Stateless Routing Algorithm with the Bi-LSTM neural network technology in a 5G network environment. the system delivers impressive results, as evidenced by simulations in MATLAB, boasting a remarkable 98.22% accuracy rate, a low 34.2 MJ energy consumption, and a high Packet Delivery Ratio (PDR) of 91%, all tested with a network of 100 nodes. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Internet of things

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Voice-Based Virtual Assistant for Windows (Ziva - AI Companion) 5

Voice-Based Virtual Assistant for Windows (Ziva - AI Compani...

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

作者： Kumar, Sunil Patel, Shubham Sonam Srivastav, Vaishnav Galgotias College of Engineering and Technology Department of Computer Science and Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350383522

ZIVA is to create a Python-based Desktop Assistant, leveraging Python's versatility and simplicity. Python is a widely used programming language in the field of Dialog Flow and AI, known for its adaptability and longstanding reputation. this Project is also based on the NLP. the primary goal of this project, named Desktop Assistant (ZIVA), is to enable users to issue commands to their local machines using voice recognition, eliminating the need for manual typing. Virtual assistants such as Siri for iOS and Cortana for Windows serve as inspiration for ZIVA. aiming to streamline the process of instructing the computer to perform various operations. ZIVA incorporates intelligent voice recognition to process user input in the form of voice commands and provide outputs such as actions to be executed or search results. Voice commands are the only way that users can engage with the assistant. ZIVA's intelligence includes the ability to store detected voice commands and match them with predefined default commands. As a Desktop Assistant, ZIVA helps with everyday tasks such as engaging in general human conversation, conducting Google searches, opening websites like YouTube, Wikipedia, and Stack Overflow, searching for and playing music, telling the time, interacting with the user, and shutting down the system. User statements and commands are analyzed using machine learning techniques to provide optimal solutions. © 2024 IEEE.

关键词： Artificial intelligence learning robotics NLP (Natural Language Processing) Advanced Engineering Virtual assistance

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Signature Verification Using Deep learning: An Empirical Study 5th

Signature Verification Using Deep Learning: An Empirical Stu...

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5th international conference on Advances in Distributed Computing and machine learning, ICADCML 2024

作者： Attri, Varinder Kaur Jaiswal, Teena Singh, Butta Bansal, Paras Sarangal, Himali Kaur, Satinder Kaur, Harmandar Department of Engineering and Technology GNDU Regional Campus Jalandhar India Department of Computer Science Guru Nanak Dev University Amritsar India

ISBN: (纸本)9789819735228

A critical step in biometric recognition is signature verification, which has applications in forensic investigations, legal papers, and financial transactions. In this study, we offer an autonomous feature extraction and learning method for convolutional neural networks (CNNs) that can automatically learn and extract pertinent features from input pictures for classification. the three key steps of our suggested methodology are pre-processing, feature extraction, and classification. To eliminate noise and distortions, we normalize the signature image and remove the backdrop during the pre-processing step. We train a CNN model to extract deep features from the processed signature picture during the feature extraction step. Finally, for classification, we use a fully linked layer. We test the proposed system using the CEDAR and CEDAR signature databases, which are both freely accessible. In comparison to state-of-the-art approaches, the experimental findings show that our suggested method is successful in reaching high accuracy 89.2%. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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A Review on YOLO Algorithms for Social Distancing 5th

A Review on YOLO Algorithms for Social Distancing

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5th international conference on Deep learning, Artificial Intelligence and Robotics, ICDLAIR 2023

作者： Kumar, Vijay Murmu, Mahendra Kumar Computer Engineering Department National Institute of Technology Kurukshetra136119 India

ISBN: (纸本)9783031609343

Object detection is one of the appealing branch of the study in the fields of computer science. Object detection defines some popular object detection models in deep learning such as YOLO (You Only Look Once), Single Sort Detector, R-CNN etc. It can be applied to solve many important challenges in computer vision, human-computer interaction such as health-care monitoring, autonomous driving, anomaly detection etc. Social distancing terminology has been coined repeatedly in-light of COVID19. Social distancing is an inherent part of health-care systems. In general, nowadays, social distancing applications have become more important due to the transmission of various diseases like smallpox, Scabies, COVID19, etc. It happens due to not maintaining of specified distance between infected peoples. the paper proposes a review of real-time applications of YOLO based object detection in social distancing. the present work is useful to understand the overview of YOLO models that can be applied for social distancing. Additionally, comparisons between the various YOLO versions with respect to different performance metrics concerning speed, accuracy, and computational complexity. these results are vital for the selection of an optimal YOLO version for real-time social distancing application. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Deep learning

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Using MediaPipe machine learning to Design Casual Exertion Games to Interrupt Prolonged Sedentary Lifestyle 5th

Using MediaPipe Machine Learning to Design Casual Exertion G...

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5th international conference on HCI in Games (HCI-Games) held as Part of the 25th international conference on human-computer interaction (HCII)

作者： Berglund, Erik Jedel, Izabella Berglund, Aseel Linkoping Univ Dept Comp & Informat Sci Linkoping Sweden

ISBN: (纸本)9783031359293;9783031359309

Casual exergames have shown potential in providing sufficient levels of exertion to decrease sedentary behavior. However, previous casual exergame implementations do not follow the guidelines of allowing for shorter (2-3 min) active breaks during more regular increments. To evaluate if casual exergames could be applied following the guidelines of shorter more regular increments we designed and developed a casual exergame using MediaPipe. the resulting game, Balloon Pump, was evaluated at a game and cosplay festival with 60 participants playing the game. After game play, perceived exertion, control and immersion, and overall experience of the game, was measured and the performance and number of movements were noted. the results from the evaluation study showed that the game was perceived as controllable, had produced sufficient levels of exertion, and was perceived as fun. the immersive experience of the game was low and the participants commented on several ways in which this aspect could be improved, such as adding more incentives and variation in the game.

关键词： Casual exergames MediaPipe Exertion Perceived Immersion Perceived Control

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Deep learning-Based Emotion Detection from EEG Signals for human-machine interaction 5

Deep Learning-Based Emotion Detection from EEG Signals for H...

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5th international conference on Information Management and machine Intelligence, ICIMMI 2023

作者： Raj, Saurabh Sakhare, Nitin N. Sharma, Yogesh Soni, Monika Bhange, Anup Deshmukh, Pradeep Kundlik Pune India Department of Computer Engineering BRACT'S Vishwakarma Institute of Information Technology Maharashtra Pune India VIIT Pune India Auricle Global Society of Education and Research Sudarshan Nagar Rajasthan Bikaner India Department of Computer Science & Engineering KDK College of Engineering Maharashtra Nagpur India Department of Computer Science and Engineering School of Computational Sciences COEP Technological University Pune India

ISBN: (纸本)9798400709418

Detecting emotions helps create more intuitive and responsive technology interfaces between humans and machines. this study introduces deep learning-based emotion detection using EEG signals. Our research uses Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and a proposed hybrid of CNN and LSTM (ConvLSTM) along with Particle Swarm Optimization (PSO) for hyperparameter optimization. the presented study aims to improve EEG-based emotion detection, which will help create more unified and emotionally intelligent human-machine interfaces. Recent deep learning advances have advanced emotion detection. these advances have allowed systems to understand and respond to human emotions. Electroencephalography (EEG) signals can capture neural patterns linked to different emotions, giving a unique view of the brain's emotional state. However, extracting meaningful emotional information from these complex signals is difficult. EEG spatial patterns are captured using CNN and LSTM networks model data temporal dependencies. A hybrid ConvLSTM model integrates spatial and temporal information for a complete EEG analysis. the study presents a method that helps to detect emotions from EEG signals with accuracy of 99.63%. this study's high accuracy is a significant advancement in the field and has great potential for improving human-machine interaction systems. Due to the use of deep learning-based emotion detection, these systems are able to intuitively respond to the feelings of their users. they allow for deeper and more genuine connections to be made between humans and machines. the presented methodology achieves remarkable precision and lays the groundwork for emotionally intelligent technologies that can impact virtual assistants, healthcare, and entertainment. this research advances emotionally intelligent human-machine interfaces, paving the way for technology to understand and respond to human emotions with precision and sensitivity. © 2023 ACM.

关键词： Electroencephalography

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the Metaverse: A Multidisciplinary Perspective on the Future of human interaction 1

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6th international conference on Applied machine learning and Data Analytics, AMLDA 2023

作者： Singh, Yuvraj Sujay, Devangana Shandilya, Shishir Kumar Shandilya, Smita VIT Bhopal University Bhopal India Devi Ahilya Vishwavidyalaya Indore India Sagar Institute of Research and Technology Bhopal India

ISBN: (数字)9783031554865

ISBN: (纸本)9783031554858

As technology advances in the path to becoming smart cities, the Metaverse emerges as a dominant potential future. By utilizing affordable high-end devices, augmented reality offers immersive internet experiences with greater capabilities. the Metaverse fosters realism, facilitating remote cooperation, immersive digital environments, enhanced interactions, and more. this paper proposes that the metaverse needs to prioritize human-centricity. In the Metaverse, it is seen that the human population comprises the largest proportion. Consequently, this study begins with providing an introduction to the origins, characteristics, and related technologies of the metaverse, along with an exploration of the concept of the human-centric metaverse (HCM). Next, we will examine the expression of anthropocentrism inside the Metaverse. Subsequently, we will now address certain current issues about the development of HCM. this study presents a comprehensive examination of the utilization of human-centric technologies inside the metaverse, together with the corresponding application scenarios for the human-Centric Metaverse (HCM). this document aims to offer academics and developers guidance and insights for the building of a human-centric Metaverse. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Augmented reality

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Facilitating Physical Activities for Elderly with Dementia in Sri Lanka: An HCI-Driven Approach 5

Facilitating Physical Activities for Elderly with Dementia i...

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5th international conference on Advancements in Computing, ICAC 2023

作者： Kariyawasam, Hasani Ruwanpathirana, thisara Kodithuwakku, Kaveen Munasinghe, Devin Ganegoda, Devanshi thilakarathna, T. Sri Lanka Institute of Information and Technology Faculty of Computing Malabe Sri Lanka

ISBN: (纸本)9798350358131

Dementia, a progressive neurological condition, erodes cognitive function and self-awareness, making individuals increasingly dependent on others. Addressing this challenge, 'person-centered care' emerged, where family or trained caregivers assist those with dementia. While previous research hinted at physical therapy's potential in dementia risk reduction, manual therapy imposes a substantial burden on both caregivers and patients. To tackle this, our research revolves around creating a smartphone app that automates and manages physical therapy as part of dementia patients' daily routines. this is achieved through a hybrid machine learning recommendation system. We also conduct a quantitative analysis to gauge the app's assistance level to both dementia patients and caregivers. Insights are gleaned through online surveys and in-person evaluations involving dementia patients, caregivers, and non-demented seniors. this informs the human-computer interaction (HCI) requirements, data collection, and testing phases. It's crucial to note that a controlled user group using the app for a month exhibited a significant improvement (> 12%) in engagement with physical therapy compared to their pre-app engagement levels. this innovation can potentially alleviate the challenges faced by dementia patients and their caregivers, improving their overall well-being. © 2023 IEEE.

关键词： machine learning

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A Comparative Analysis of Child Activity Recognition Using machine learning 5th

A Comparative Analysis of Child Activity Recognition Using M...

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

作者： Kumar, Narendra Arora, Shefali Aggrawal, Lakshay Computer Science and Engineering National Institute of Technology Punjab Jalandhar144008 India Computer Science and Engineering DAV Institute of Engineering and Technology Punjab Jalandhar144008 India

ISBN: (纸本)9789819761050

this research study aims to create a smart child activity recognition device combines a Raspberry Pi-pico, sensors for Internet of things (IoT). the system includes several sensors for monitoring human motion, pulse, and location parameters. Different types of machine learning and deep learning algorithm is used for analyzing data to identify various kinds of activities in children including sitting, standing, running, and climbing the stairs. this study utilizes a Raspberry Pi-pico, which is a cost-effective as well as tiny single-board computer central processing unit for an infant monitoring system. It can be easily integrated with various sensors and devices, which make it a suitable foundation for developing an IoT-based tracking system. It is able to link to the Internet through Wi-Fi, allowing for surveillance remotely. the dataset collected from 22 different children based on their features like sitting, standing, running, and climbing the stairs. the model was trained using a data of child mobility and behavior, distinguishing activities such as sitting, standing, running, and stair climbing. there are six different classifiers: LSTM, SVM, ANN, KNN, AdaBoost, and Logistic Regression. When applied to pre-processed data, SVM outperformed other classification methods. the classification accuracy reached 98.81%. the experimental results indicate that the proposed system may identify behavior and it will keep parents remotely. the technology continuously monitors and analyzes the child’s environment and behavior to ensure their safety and well-being. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Support vector machines

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