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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221-230 订阅

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Adaptive Movie Recommendations: A Deep learning Framework with Mood and Sentiment Integration 5

Adaptive Movie Recommendations: A Deep Learning Framework wi...

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

作者： Karthikeyan, M. Saravana Shankar, S. Vijay Krishnan, R. Santhana Sornavalli, M. Raj, J. Relin Francis Sundaravadivel, P. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Sona College of Technology Department of Electrical and Electronics Engineering Salem India SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Velammal College of Engineering and Technology Department of Mathematics Madurai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Saveetha Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798331530013

this research study proposes an advanced movie recommendation system that combines deep learning models with mood detection and sentiment analysis for highly personalized suggestions. the system collects data from diverse sources, including user watch history from OTT platforms along with real-time social media sentiment from social media platforms. Additionally, wearable devices track heart rate to infer the user's emotional state during media interaction. the recommendation engine uses a multi-layered approach. Neural Collaborative Filtering (NCF) analyzes watch patterns, while BERT-based embeddings capture key movie metadata such as genres and actors for content-based filtering. the system also employs an LSTM model for mood-based recommendations, integrating heart rate data and social media sentiment to align suggestions with the user's current mood. Key advantages include a hybrid recommendation approach that combines collaborative filtering, content-based suggestions, and real-time mood analysis. the system adapts to both long-term preferences and short-term emotional states, delivering more relevant and personalized results. Deployed on scalable cloud infrastructure (AWS EC2), with continuous learning from user feedback, the system ensures dynamic and evolving movie recommendations that enhance user satisfaction. © 2024 IEEE.

关键词： Collaborative filtering

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Intelligent vehicle classification system based on deep learning and multi-sensor fusion 5

Intelligent vehicle classification system based on deep lear...

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5th international conference on computer Vision and Data Mining, ICCVDM 2024

作者： Li, Xinjin Yang, Yuanzhe Yuan, Yixiao Ma, Yu Huang, Yangchen Ni, Haowei Columbia University New YorkNY United States Diablo Valley College Pleasant HillCA United States Carnegie Mellon University PittsburghPA United States

ISBN: (纸本)9781510682863

With the rapid development of Intelligent Transportation Systems (ITS), vehicle type classification, as a key link in Automatic Toll Collection systems (ATC), is of great significance in improving traffic efficiency and reducing economic losses. this study proposes an intelligent vehicle classification system based on deep learning and multi-sensor data fusion to address the accuracy issues existing in vehicle classification methods based on optical sensors (OS) and human observers. the system significantly improves the accuracy and robustness of vehicle classification by combining deep Convolutional Neural Networks (CNN), LiDAR sensors, and machine learning algorithms. We first constructed a large-scale annotated dataset containing multiple vehicle types and complex traffic scenes to improve our model's capability to identify different vehicle characteristics. Next, CNN models based on different architectures were designed to extract global and local features of the vehicle, respectively. In addition, the LiDAR sensor was used to achieve the spatial structure architectures of the vehicle and combined with the output of the CNN model to improve classification performance under occlusion and complex scenes. © 2024 SPIE.

关键词： Convolutional neural networks

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Enhancing Real-Time Stream Processing with Apache Kafka by integrating Scaled-Hidden Markov Models 5

Enhancing Real-Time Stream Processing with Apache Kafka by i...

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

作者： Rachana, V. Sanjana, S. Jayashree, R. PES University Department of Computer Science and Engineering Bangalore India Bangalore India

ISBN: (纸本)9798331518097

this research study explores the integration of Kafka, a distributed streaming platform, in the context of enhancing speech recognition systems utilizing Hidden Markov Models (HMMs). Speech recognition is a vital aspect of AI and machine learning which plays an important role in the interaction between humans and computers. Hidden Markov models (HMMs) are a popular choice for speech recognition systems due to sequential data modeling. In this paper, we conduct a comprehensive study integrating Big Data technologies like Kafka into Hidden Markov Model (HMM) based speech recognition, aiming to recognize spoken words from audio wave files and classify them into predefined word classes. the integration involves a multifaceted approach, employing Kafka for efficient data processing and communication between distinct system modules. To prevent underflow during HMM training, a Scaled HMM Module is employed. We address challenges posed by unseen data, by incorporating smoothing techniques such as add-delta smoothing which mitigates zero probabilities and improves the generalization of the model. We also experiment the use of pruning techniques which disregards the low-probability paths during the Viterbi algorithm, which results in improvement in the accuracy. By incorporating the integration of Big Data technologies like Kafka, along with advanced techniques such as pruning, smoothing, and scaling of Hidden Markov Models (HMMs), we delve into a comprehensive strategy for advancing speech recognition capabilities. © 2024 IEEE.

关键词： Hidden Markov models

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Facial Features Recognition and Classification Using machine learning Model 1

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8th international conference on Data Management, Analytics and Innovation, ICDMAI 2024

作者： Amutha, S. Pratap, Shashank School of Computer Science Engineering VIT Chennai Chennai India

ISBN: (数字)9789819732425

ISBN: (纸本)9789819732418

this research-based project introduces a methodology for accurate and real-time facial recognition and feature detection. Leveraging machine learning algorithms and Haar cascades, the system achieves high accuracy in classifying faces using a trained support vector classification (SVC) model. Real-time detection of facial features, including eyes and smiles, is accomplished through the implementation of Haar cascades. the proposed methodology is evaluated using the Labeled Faces in the Wild (LFW) dataset, demonstrating its effectiveness in various applications such as security systems, user interfaces, and human–computer interaction. Despite challenges related to privacy, biases, and environmental factors, the project offers avenues for future research and improvements. Responsible development and ethical deployment of facial recognition technology are emphasized to ensure its positive impact on society. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Face recognition

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BioPoint: Enhancing human-computer interaction through Single-Site, Multi-Sensor Gesture Recognition 46

BioPoint: Enhancing Human-Computer Interaction through Singl...

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46th Annual international conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Isabel, Xavier Campbell, Evan Olivier, Loïc Gagné, Gabriel Scheme, Erik Côté-Allard, Ulysse Gagnon-Turcotte, Gabriel Laval University Dept. of Electrical and Computer Engineering QC Canada University of New Brunswick Department of Electrical and Computer Engineering FrederictonNBE3B 5A3 Canada University of Oslo Dept. of Technology Systems Oslo Norway

ISBN: (纸本)9798350371499

Advancements in human-computer interaction (HCI) and machine learning are seen as key avenues to help individuals living with upper limb disabilities in accomplishing their activities of daily living. Multi-channel myoelectric systems are a promising approach for HCI due to their intuitive and accurate capture of user intent through muscle activity. However, such systems are still bulky compared to widely accepted smartwatches-like devices and as such pose a challenge for seamless integration into daily life. Consequently, this work presents the first single-site EMG-IMU-PPG system for multi-gesture recognition from the wrist. Overall, when evaluated on eight able-bodied participant, the system achieves an average accuracy of 65.28%±10.45% on 17 finger and wrist gestures and 90.98%±5.65% on eight hand gestures. this study also introduces "Myo Hero", an online environment inspired by "Guitar Hero."Myo Hero provides a dynamic method for testing multi-gesture recognition systems, improving the evaluation process's appeal. Online evaluation of the multimodal system substantially outperformed random play, signifying EMG-IMU-PPG's potential for human-computer interaction. © 2024 IEEE.

关键词： Electromyography

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A machine learning Based Video Summarization Framework for Yoga-Posture Video 5th

A Machine Learning Based Video Summarization Framework for ...

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5th international conference on Computational Intelligence in Communications and Business Analytics, CICBA 2023

作者： Afreen, Sana Ghosh, Tanmoy Bhattacharyya, Soumya Bhar, Anirban Saha, Sourav Narula Institute of Technology West Bengal Kolkata India

ISBN: (纸本)9783031488788

Video summarization techniques aim to generate a concise but complete synopsis of a video by choosing the most informative frames of the video content without loss of interpretability. Given the abundance of video content and its complex nature, there has always been a huge demand for an effective video summarization technique to analyze various dynamic posture centric videos. Yoga session video summarization is one of the interesting application areas of dynamic posture centric video analysis that is lately drawing the attention of computer vision researchers. the majority of available general video summarizing methods fail to detect key yoga poses in a yoga session video effectively, as they do not consider posture-centric information while extracting key frames. In this paper, we propose a machine learning based video summarization framework, which is capable of extracting a series of key postures in a yoga session video by tracking a few key-posture points corresponding to vital parts of the human body. Compared to the widely used FFMPEG tool, the proposed method appears to have a higher proportion of matched keyframes but a lower proportion of missing key-frames and redundant non key-frames with respect to the ground truth set, demonstrating its potential as an effective yoga posture video summarizer. © 2024, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： machine learning

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machine learning for Drug Discovery: Predicting Drug-Protein Binding Affinities using Graph Convolutional Networks 5

Machine Learning for Drug Discovery: Predicting Drug-Protein...

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5th IEEE international conference on Recent Trends in computer Science and Technology, ICRTCST 2024

作者： Radhamahendran, S. Dogra, Akriti Mendhe, Dinesh Tilak Babu, S.B.G. Dixit, Shriniket Singh, Surya Pratap Vels Institute of Science Technology and Advanced Studies School of Life Sciences Department of Bioinformatics Chennai Pallavaram India Manav Rachna Dental College Department of Oral & Maxillofacial Surgery Haryana Faridabad India State University of New Jersey Computer and Information Research Scientist Office of Research Computing Rutgers New BrunswickNJ United States Aditya Engineering College Department of ECE Surampalem India Vellore Institute of Technology School of Computer Science Engineering Tamil Nadu Vellore India UPUMS Department of Multi-Disciplinary Research Unit UP Etawah India

ISBN: (纸本)9798350351378

Determining the potential interactions between ligands and proteins is a crucial first step in developing new drugs. Graph BAR, our novel prediction model, makes accurate guesses about the strength of ligand-protein bindings by means of a graph convolutional neural network. When compared to graph convolutional neural networks, standard convolutional neural network models are more resource and time intensive to manage. the interaction between proteins and ligands is shown via a network consisting of many adjacency meshes. the atomic distances dictate the alterations to these matrices. Additionally, the atomic properties can be shown in a feature matrix. Using the PDBbind datasets, we verified the functionality of graph convolution and evaluated Graph BAR's ability to forecast the binding strengths of proteins and ligands. In order to maximize the model's performance was improved by utilizing ways for adding additional input, which took advantage of the processing capability of graph convolutional neural networks. Graph BAR's predictions appear to have potential for use in drug creation due to its great accuracy and quickness. the results of our research suggest that Graph Convolutional Networks have the potential to expedite the discovery of new medications by properly predicting the interactions between pharmaceuticals and proteins. © 2024 IEEE.

关键词： Proteins

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Research on Image Recognition System Based on computer Artificial Intelligence Technology 4

Research on Image Recognition System Based on Computer Artif...

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4th IEEE international conference on Power, Electronics and computer Applications, ICPECA 2024

作者： Xu, Zhihua School of Design and Art Jingdezhen Ceramic University Jingdezhen China

ISBN: (纸本)9798350359589

this paper uses mobile network mode to develop and model Python crawler based on Ubuntu16.04 and CUDA9.0. the visualization of this pattern was achieved using Tensor Board software. the hardware design of the system consists of three modules: linear array CCD sensor, DSP processor and signal processing circuit. through the reconstruction and learning of such models, the trained models are stored, so as to obtain higher precision image recognition. A method of classifying objects using Fourier description operators is proposed. the experimental results show that the accuracy of the image is 96.8% and the recognition time is 0.55 seconds. Motion tracking is based on the positioning and capture of the specific position of the human hand in each screen, and the captured frame rate is 28 frames per second. the system can quickly acquire images of 1024∗1500 pixels at a rate of 5 photos/second. through the static identification of posture and tracking of motion trajectory, the interaction between human and machine is better. © 2024 IEEE.

关键词： artificial intelligence digital signal processing fourier Image identification technology mobile network

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Prediction of Chronic Kidney Disease Using ML Techniques 5

Prediction of Chronic Kidney Disease Using ML Techniques

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

作者： Shukla, Anupam Robin, Fabian Narvaria, Harshit Joshi, Anju Computer Science and Engineering Abes Engineering College Ghaziabad India

ISBN: (纸本)9798350383522

the study of chronic kidney disease of human body is now a significant topic of interest in the domain of biosciences because of the emerging cases of the disease. One of the most dangerous illnesses in the modern world is chronic kidney disease, for which an accurate diagnosis method needs to be developed to identify and stop the disease at an early stage. A condition known as chronic kidney disease causes a person's kidneys to malfunction and eventually become damaged. this leads to the condition where the kidney is unable to filter the blood and remove the waste as they do. Now-a-days machine learning algorithms have become a major tool in which the disease gets diagnosed based on the certain values like blood pressure, urine value and many such things. this leads to the early detection of the disease so that the patient can consult his required doctor and with the help of which his disease could be cured. the application and the study of the applied algorithm will help us to detect the disease will be explored in this study. these algorithms' outputs have a higher degree of accuracy, sensitivity, and specificity and can be used to predict the presence of chronic kidney disease in the necessary human being. the findings will demonstrate how technology can aid in the better management of chronic kidney disease and how this can enhance decision support systems for healthcare in general. © 2024 IEEE.

关键词： Decision tree GUI Data-driven detection Smart disease detection machine learning methodologies SVM

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User Identification via Free Roaming Eye Tracking Data 26th

User Identification via Free Roaming Eye Tracking Data

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26th international conference on human-computer interaction (HCII)

作者： Haria, Rishabh Vallabh Varsha El Abed, Amin Maneth, Sebastian Univ Bremen Dept Informat Bremen Germany

ISBN: (纸本)9783031621093;9783031621109

We present a new dataset of "free roaming" (FR) and "targeted roaming" (TR): a pool of 41 participants is asked to walk around a university campus (FR) or is asked to find a particular room within a library (TR). Eye movements are recorded using a commodity wearable eye tracker (Pupil Labs Neon at 200 Hz). On this dataset we investigate the accuracy of user identification using a previously known machine learning pipeline where a Radial Basis Function Network (RBFN) is used as classifier. Our highest accuracies are 87.3% for FR and 89.4% for TR. this should be compared to 95.3% which is the (corresponding) highest accuracy we are aware of (achieved in a laboratory setting using the "RAN" stimulus of the BioEye 2015 competition dataset). To the best of our knowledge, our results are the first that study user identification in a non laboratory setting;such settings are often more feasible than laboratory settings and may include further advantages. the minimum duration of each recording is 263 s for FR and 154 s for TR. Our best accuracies are obtained when restricting to 120 s and 140 s for FR and TR respectively, always cut from the end of the trajectories (both for the training and testing sessions). If we cut the same length from the beginning, then accuracies are 12.2% lower for FR and around 6.4% lower for TR. On the full trajectories accuracies are lower by 5% and 52% for FR and TR. We also investigate the impact of including higher order velocity derivatives (such as acceleration, jerk, or jounce).

关键词： User Identification Eye Movements Data machine learning

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