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Real-Time Facial Expression Recognition Using Convolutional Neural Networks for Adaptive User Interfaces 5

Real-Time Facial Expression Recognition Using Convolutional ...

引用

5th ieee international conference for Emerging Technology, INCET 2024

作者： Sundi, Jerry Jatan Kumar, Hitesh Bharti Bedi, Rajnish Department of Computer Science and Engineering Chandigarh University Mohali India Department of Computer Science and Engineering Chandigarh University Mohali India

ISBN: (纸本)9798350361155

Facial expression recognition plays a crucial role in enabling natural and intuitive human-computer interaction. However, existing approaches often struggle with robustness and accuracy, particularly in unconstrained environments with varying illumination, occlusions, and pose variations. This research proposes a novel real-time facial expression recognition system based on convolutional neural networks (CNNs) to address these challenges. The proposed system leverages state-of-the-art deep learning architectures and techniques to accurately recognize a wide range of facial expressions in real-time. Through extensive data preprocessing, augmentation, and a carefully designed CNN architecture, the system aims to achieve high accuracy and generalization capabilities across diverse scenarios. The recognized facial expressions are then utilized to enable adaptive user interfaces that can dynamically adapt to the user's emotional state, enhancing the overall user experience. Comprehensive experiments and user studies are conducted to evaluate the system's performance, robustness, and effectiveness in improving human-computer interaction. The proposed approach has the potential to significantly impact various domains, including gaming, healthcare, education, and beyond, by enabling emotion-aware computing and personalized interfaces. © 2024 ieee.

关键词： Real time systems

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Adaptive Video Task Offloading Based on Mobile Vehicle Assistance 7

Adaptive Video Task Offloading Based on Mobile Vehicle Assis...

引用

7th ieee Information Technology, Networking, Electronic and automation Control conference, ITNEC 2024

作者： Ma, Mengyuan Wang, Luyao Ma, Shizhao Feng, Shuquan College of Computer and Information Engineering Tianjin Normal University Tianjin China

ISBN: (纸本)9798350370782

With the increasing number of Internet of Things(IoT) devices, many computationally intensive vedio tasks need to be processed. Mobile edge computing(MEC) has emerged as an effective solution, but a large number of real-time video tasks are still in the queue especially in congested traffic areas or busy periods. Considering the abundance of idle computing resources in moving vehicles on roads, this paper utilizes moving vehicles to assist edge servers in offloading video tasks. Firstly, the dwell time of vehicles calculated by the movement trajectory of vehicles is used to select candidate vehicles for task offloading. Secondly, the channel quality for video task transmission is analyzed and network bandwidth is predicted. Additionally, a QoE model is constructed, and we propose a bitrate adaptive selection algorithm that can transmit videos with the highest possible resolution. The simulation proves that our proposed method can provide more efficient and stable offloading services. © 2024 ieee.

关键词： Video streaming

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Deep Learning Techniques for Weather Prediction

Deep Learning Techniques for Weather Prediction

引用

2024 international conference on System, Computation, automation and Networking, ICSCAN 2024

作者： Dhanalakshmi, R. Paul, Tushar School of Computer Science and Engineering Vellore Institute of Technology Centre for Cyber Physical Systems Chennai India Masters of Computer Application Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331510022

Human activities are directly impacted by weather occurrences. Forest fires, high air temperatures that lead to drought, and extreme weather events caused by global warming all make life more difficult for humans. Precautions against such climatic calamities require weather forecast models that are both accurate and effective. As a result, creating models that accurately forecast the weather is crucial. The creation of effective models of deep learning-based weather forecasting was greatly aided by technological advancements. This study suggested a hybrid weather prediction model based on convolutional and recurrent neural networks, which had a decent success rate. The dataset named Jena, which comprises 14 parameters, extensive meteorological information that has not been used for predicting the weather, was subjected to the suggested hybrid model. The experimental findings were compared using well-known machine learning, deep learning, and statistical techniques such CNN, Integrated Moving Average with Auto-Regression, LSTM, RNN, Random Forest, Multilayer Perceptrons and SVM. These comparisons showed that the suggested hybrid model had the best prediction outcome across the board. For instance, the suggested hybrid model produced the following findings based on the weather forecast data for Jena. © 2024 ieee.

关键词： Convolutional neural networks

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An Outlier Rejection Network for Point Cloud Registration with SC2-Based Seed Selection 14

An Outlier Rejection Network for Point Cloud Registration wi...

引用

14th ieee international conference on Cyber Technology in automation, Control, and Intelligent Systems, CYBER 2024

作者： Guo, Shiyi Jia, Tong Zhang, Jiayu Zhou, Yunchun Yang, Tao College of Information Science and Engineering Northeastern University Shenyang110819 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China

ISBN: (纸本)9798331506056

Recently, feature-based point cloud registration algorithms have gained more attention for their robustness. Outlier rejection is an essential step in it, which aims to remove outliers from correspondences established by point cloud feature matching. Outlier rejection for point cloud registration has been widely studied. However, the accuracy of learning-based outlier rejection methods still need to be improved. In this article, we propose an outlier rejection network for point cloud registration, which consists of seed selection, feature embedding, consistent set construction, local spectral matching and hypothesis selection modules. Particularly, a novel seed selection module based on the second order spatial consistency (SC2) is proposed. Experimental results on various datasets show that the proposed network achieves better accuracy than previous methods. © 2024 ieee.

关键词： Cloud platforms

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Meteoroid recognition algorithm of civil astronomical meteor monitoring system based on meteor trajectory recognition and light curve 5

Meteoroid recognition algorithm of civil astronomical meteor...

引用

5th international conference on Robotics and computer Vision, ICRCV 2023

作者： Shao, Qi Han, Xiao Lei Fan, Jia Nan Zhang, Xin Long Wang, Nan Internet of Things Engineering Department Shanghai Business School Shanghai China Beigene Ltd Shanghai China Computer Science and Technology Shanghai Business School Shanghai China Digital Publishing、Academic Resources Import and Export China Science Publishing & Media Ltd Beijing China

ISBN: (纸本)9798350326369

Due to the decentralized nature of civil meteor monitoring and the lack of conditions for large-scale observations like in scientific astronomical meteor monitoring, the identification algorithms for scientific astronomical meteor monitoring cannot be applied to civil meteor monitoring. In response to the problem that the civil meteor monitoring system cannot effectively distinguish meteors from other moving objects during meteor recognition, a meteor candidate recognition algorithm has been designed and implemented. This algorithm mainly includes two parts: meteor trajectory recognition and light change curve shape analysis. By using this meteor recognition algorithm to process two months' worth of images from the civil meteor monitoring system, 7400 meteor trajectories were extracted, of which 25% were non-meteor objects. The algorithm classifies the observed moving objects into target sets, and by considering the features of the light variation curve's single-peak structure and multi-frame features, the final algorithm feature accuracy is 86%. © 2023 ieee.

关键词： Trajectories

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Emerging ICT Trends in engineering Structures and Architectural Economics: The Role of Human-computer Interaction in Shaping Future Design and Efficiency 4

Emerging ICT Trends in Engineering Structures and Architectu...

引用

4th ieee international conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Shrivastava, Anurag Nainwal, Ankita Shnain, Ammar Hameed Yadav, Kanchan Pushkarna, Gaurav Harikrishna, M. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamilnadu Chennai India Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Gla University Department of Electrical Engineering Mathura India Lovely Professional University Phagwara India Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798331518981

This study looks into the new trends in information and communication technology (ICT) for engineering structures, focussing on how these trends interact with architectural economics and how important human-computer interaction (HCI) is to the efficiency and design of the future. The integration of information and communication technology (ICT) in structural engineering and architectural design has become essential for optimising both functionality and aesthetics due to the rapid growth of digital technologies. The study investigates how modern computational approaches, virtual reality (VR), and building information modelling (BIM) tools improve stakeholder collaboration and design precision. Furthermore, a study of HCI principles shows how important they are for enhancing utility and user experience, as well as for making sure that technical solutions successfully satisfy the demands of clients, engineers, and architects. HCI enhances efficiency in decision-making and productivity by emphasising usability in design tools and platforms. The results show that creative frameworks that are resilient, durable, and economically feasible can be created by strategically utilising ICT in conjunction with HCI. The goal of this research is to offer a thorough framework for comprehending how different fields intersect, ultimately opening the door to a more effective and progressive environment for engineering and architecture that places a higher priority on user-centred design. © 2024 ieee.

关键词： Architectural design

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Surgical Gesture Recognition in Open Surgery Based on 3DCNN and SlowFast 7

Surgical Gesture Recognition in Open Surgery Based on 3DCNN ...

引用

7th ieee Information Technology, Networking, Electronic and automation Control conference, ITNEC 2024

作者： Men, Yutao Luo, Jian Zhao, Zixian Wu, Hang Luo, Feng Zhang, Guang Yu, Ming Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China Tianjin300384 China Systems Engineering Institute Academy of Military Sciences People's Liberation Army Tianjin300161 China

ISBN: (纸本)9798350370782

In the field of computer-aided medicine, automated surgical gesture recognition is an important research direction. Its main applications include automated skills assessment, intraoperative guidance, and education and training systems. Current research has focused on robotic surgery, and there is less research on surgical gestures for open surgery. Therefore, in this study, a new simulated open surgery dataset was developed, and a series of surgical gestures for open surgery were defined for closure the abdomen surgery with the cooperation of professional doctors. In addition, this paper uses the classical algorithm 3DCNN and SlowFast model in the field of action recognition to perform offline recognition of surgical gestures for open surgery on the dataset. 3DCNN uses R3D and R(2+1)D models. The experimental results show that on the self-built open surgical dataset, the R3D model has the best surgical gesture recognition results, with an accuracy rate of 90.4%, a precision rate of 90.5%, and a recall rate of 90.0%. © 2024 ieee.

关键词： Gesture recognition

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Machine Learning Model for Sentiment Analysis on Mental Health Issues 15

Machine Learning Model for Sentiment Analysis on Mental Heal...

引用

15th international conference on computer and automation engineering, ICCAE 2023

作者： Kaushik, Baijnath Sharma, Akshita Chadha, Akshma Sharma, Reya Shri Mata Vaishno Devi University Computer Science and Engineering Katra India

ISBN: (纸本)9798350396225

Social media study concerning mental health has increasingly piqued researchers' interest. One of the most well-known sites where users can express their ideas, feelings, and opinions is Reddit. Research statistics on important topics are available on social media. People's mental health is one of the main issues because it is a new area of interest. Different issues, including anxiety, tension, anger, and despair, can be brought on by mental disorders. In recent years, people appear to be busy and have less time to contact one another. Instead, they seem to prefer to engage in online discussion forums. Data has been collected from social networking sites like Reddit, and 10,000 posts were aggregated for investigating the posts among suicidal and non-suicidal. Machine learning algorithms such as Support Vector Machine, Logistic Regression, and Multinomial Navïe Bayes segregated the posts into two classes. Pre-processing of data is done which is the vital step in text analysis, which includes tokenization, removal of stop words and special characters, etc. The performance in terms of accuracy and precision Logistic Regression outperforms the other algorithms. Multinomial Naive Bayes yields great recall. The study also signifies the current research trends and proffers an overview of the researcher's accomplishments in the related fields. © 2023 ieee.

关键词： Logistic regression

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Improved UNET Architecture for Effective COVID-19 Severity Prediction from Pulmonary CT Images 1

Improved UNET Architecture for Effective COVID-19 Severity P...

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1st international conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Ramla, M. Jasmine, R. Anita Ramesh, M. Bai, Femilda Josephin Joseph Shobana SRM Institute of Science and Technology Department of Computer Applications Chennai India SRM Institute of Science and Technology Department of Data Science and Business Systems Chennai India SRM Institute of Science and Technology Department of Computer Science Chennai India Istinye University Department of Computer Engineering Istanbul Turkey

ISBN: (纸本)9798350376517

COVID-19, is one of the deadliest pandemic hit globally crises in the recent years, continues to persist with emerging variants of concern. Due to the virus's high transmissibility, rapid and precise detection of COVID-19 is crucial for timely medical intervention. This research presents an advanced deep transfer learning approach for quantifying the severity of COVID-19 infection from CT lung images. Using computer vision techniques, lung regions were extracted from CT scans through semantic segmentation using SegNet. An enhanced U-Net architecture, integrated with the Atrous Spatial Pyramid Pooling (ASPP) technique, was employed to semantically segment the infected regions of the lungs. Based on the segmented infection areas, the severity of the infection was classified into mild, moderate, and severe. The proposed framework, which incorporates lung extraction followed by Ground-Glass Opacity (GGO) segmentation, achieves the segmentation accuracy of 96% and a prediction accuracy of 98.9%. © 2024 ieee.

关键词： COVID-19

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Accurate Photovoltaic Forecasting through a Multi-connected Structural Network 4

Accurate Photovoltaic Forecasting through a Multi-connected ...

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

作者： Xie, Jiangshan Hu, Yanming Rong, Fangying International Business School Henan University Zhengzhou China School of Information Communications Engineering Beijing Information Science & Technology University Beijing China School of Information Science and Engineering Shandong Agricultural University Tai'an China

ISBN: (纸本)9798350359589

With the growing demand for renewable energy sources, the importance of accurate photovoltaic (PV) forecasting has become crucial for effective energy management. Predicting PV output remains a challenge due to the need for high accuracy and low computational complexity. This paper introduces a novel method for general PV forecasting through a multi-connected structural network to pursue accurate PV generation. We explore the impact of different neural network interactions on actual power data within the network layer and test its relevance by constantly changing parameters. Stacked gate recurrent unit (GRU) and bidirectional long short-term memory (BiLSTM) networks are used to learn universal meteorological data feature as input for network layers, and after incorporating an attention mechanism, they are trained in a hierarchical manner of multi model fusion, interaction, and infiltration. Experimental results indicate that the proposed method can achieve competitive performance when compared to traditional single approaches. © 2024 ieee.

关键词： Attention CNN Feature fusion PV forecasting

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