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检索条件"任意字段=International Conference on Computer Engineering and Technology"

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Sentiment classification of MOOC courses by merging local context focus and Bi-directional gated recurrent unit 4

Sentiment classification of MOOC courses by merging local co...

引用

4th international conference on computer Vision, Application, and Algorithm, CVAA 2024

作者： Jin, Jiayong Dong, Yongquan He, Bugui Zhou, Nan Yan, Linke School of Computer Science and Technology Jiangsu Normal University Jiangsu Xuzhou221116 China Xuzhou Cloud Computing Engineering Technology Research Center Jiangsu Xuzhou221116 China Jiangsu Engineering Technology Research Center of ICT in Education Jiangsu Xuzhou221116 China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

Existing MOOC review sentiment classification methods do not fully utilize the local context information associated with aspect, and they ignore the connection between local and global contexts, while resulting in modeled features that lack the information connection between aspect and contexts. In this paper, we propose a model that incorporates Local Context Focus (LCF) and Bi-Directional Gated Recurrent Unit (Bi-GRU). First, the BERT model is used to dynamically encode course reviews. Then, global semantic features are extracted using the Bi-GRU model to strengthen the connection between the preceding and following texts. Then, the LCF model based on multi-head self-attention is used to obtain local contextual features and splice them with global semantic features. Finally, the Softmax function is utilized to output the classification results. The experimental accuracies of the proposed model on the three MOOC course review datasets reach 97.96%, 96.76%, and 94.16%, respectively, which are improved by 0.70%, 0.42%, and 0.03% over the suboptimal baseline model. The proposed model significantly improves the effectiveness of MOOC course review sentiment classification, and provides a useful reference for the optimization and improvement of MOOC courses. © 2025 SPIE.

关键词： Semantics

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Ethical Framework for Multimodal AI Systems 1

Ethical Framework for Multimodal AI Systems

引用

1st international conference on AIML-Applications for engineering and technology, ICAET 2025

作者： Patkar, Uday Chandrakant Pitrubhakta, Vinit Sutar, Sanket Arora, Aria Jaiswal, Ritika Bhardwaj, Shikha Patil, Uday Shankar Bharati Vidyapeeth's College of Engineering Lavale Computer Department Pune India Bharati Vidyapeeth's College of Engineering Lavale Pune India Bharati Vidyapeeth's College of Engineering Lavale Department of Engineering science Pune India Bharati Vidyapeeth's College of Engineering Lavale Head of civil Engineering Department Pune India

ISBN: (纸本)9798350355611

As artificial intelligence (AI) systems converge on data from different modalities such as text, audio and images, this leads to a very unique ethical challenge. This research paper provides a theoretical framework to address the ethical implications of multimodal AI system. This paper focuses on three domains: privacy, fairness and consent. This paper reflects on various ways to protect sensitive data, mitigate biases across different modalities and ensure consent and transparency. These are accompanied by guidelines which are provided to support the best practices of responsible design and deployment of these systems. The aim of this paper is to help engineers develop multimodal AI technologies that are ethically sound and socially beneficial. © 2025 IEEE.

关键词： Differential privacy

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OTFS technology for the Internet of Things in the Context of High-Speed Mobility 24

OTFS Technology for the Internet of Things in the Context of...

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8th international conference on Electronic Information technology and computer engineering, EITCE 2024

作者： Liu, Mingming Wang, Guofu Jia, Xiaobo Ye, Jincai School of Automation Guangxi University of Science and Technology Guangxi Liuzhou China School of Electronic Engineering Guangxi University of Science and Technology Guangxi Liuzhou China School of Information and Communication Guilin University of Electronic Technology Guangxi Guilin China

ISBN: (纸本)9798400710094

This paper addresses the Doppler shift challenge inherent to Orthogonal Time-Frequency Space (OTFS) communication techniques in high-speed mobile scenarios of the Internet of Things (IoT). First, the performance of Quadrature Amplitude Modulation (QAM) technology is analyzed, and the Bit Error Rates (BER) of OTFS, Orthogonal Frequency Division Multiplexing (OFDM) under different QAM modulation modes are compared. It is concluded that when the OTFS transmits a large amount of data, the larger the QAM modulation order, the smaller the BER, and the better the transmission effect. Secondly, the BER trends of OTFS with OFDM at different speeds are compared. The results demonstrate that OTFS exhibits superior anti-Doppler performance compared with OFDM at high-speed moving speeds. Finally, the BER performances of the three detection methods, namely, MP, LMMSE, and MRC delay-Doppler, are investigated to analyse the performance of the BER under different QAM modulations. The results demonstrate that the MRC delay-Doppler detection method is more effective in reducing the BER for the DD domain, exhibiting a robust capacity to compensate for the Doppler frequency deviation and facilitate superior signal restoration. © 2024 Copyright held by the owner/author(s).

关键词： Quadrature amplitude modulation

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Contextual Object Detection Using Deep Learning 1

Contextual Object Detection Using Deep Learning

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1st international conference on AIML-Applications for engineering and technology, ICAET 2025

作者： Gulhane, Anushka Milind Patil, Dipti Chimalgi, Vinita Information Technology MKSSS's Cummins College of Engineering for Women Pune Pune India

ISBN: (纸本)9798350355611

Object detection has been widely used in the field of computer vision for a variety of applications. It has become even more essential in the last decade helping us detect and eventually classify various objects. In this paper, our primary focus is graphical objects like Logos and Signatures present within official documents, detecting them, and understanding their significance. We have used EfficientDet, a network for object detection, and have built on it by extracting required features using our regions of interest. EfficientDet has been a groundbreaking model that came about in recent years and has been subsequently used in the detection of various components in an image. In this paper, we have extended the network's functionality to textual documents that contain graphical elements. The system has been designed specifically for official documents like insurance forms, bills, cheques, etc. © 2025 IEEE.

关键词： Deep learning

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Optimizing Blood Bank Management with Cloud-Hosted Long Short-Term Memory Models for Inventory Forecasting and Utilization 3

Optimizing Blood Bank Management with Cloud-Hosted Long Shor...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Devi, R. Aiyshwariya Sree Southry, S. Rajanarayanan, S. Sekaran, S. Chandra Sivaraman, Audithan Srinivasan, S. R.M.K. College of Engineering and Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Sona College of Technology Department of Electronics and Communication Engineering Tamil Nadu Salem India Department of Computer Science and Engineering Tamil Nadu Salem India P.S.V College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Krishnagiri India Department of Information Technology Jafferpur New Delhi India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509675

This research presents an innovative method for blood bank management using Cloud-based Long Short-Term Memory (LSTM) models for precise inventory forecasting and optimization. The objective of this research is to increase blood bank efficiency by utilizing LSTM models to accurately estimate demand, optimize inventory levels, and enhance overall resource utilization. The system uses cloud computing to provide real-time demand forecasting, minimizing blood waste and assuring a reliable supply for healthcare requirements. LSTM models use historical data to identify long-term trends and variations in blood demand, facilitating accurate inventory forecasting. This predictive system optimizes the allocation process, resulting in improve resource utilization and increased operational efficiency. In compared to traditional techniques, the proposed system provides a more scalable and flexible solution by using cloud technology to provide continuous monitoring and dynamic modifications of inventory levels. It provides a robust framework for management of blood supply and enhancing patient care for improved resource management. Future enhancements may include broadening the system to incorporate real-time multi-source data integration and investigating hybrid machine learning models to better optimize demand forecasts. © 2025 IEEE.

关键词： Efficiency

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Adaptive Quantum and Graph-Attentive Security Framework for DoS Attack Mitigation in VANETs 2

Adaptive Quantum and Graph-Attentive Security Framework for ...

引用

2nd international conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Anjum, Asma Rao, P. S. V. Srinivasa Sindhu, M. Bhatt, Pooja Priya, M.R. Maranan, Ramya HKBK College of Engineering Department of Computer Science and Engineering Karnataka Bengaluru560045 India Joginpally B R Engineering College Department of Computer Science and Engineering Telangana Hyderabad500075 India R.M.K Engineering College Department of Computer Science and Business Systems Tamil Nadu Thiruvallur601206 India Parul institute of Engineering and Technology Parul University Computer Science and Engineering Faculty of Engineering and Technology Post Limda Gujarat 391760 India NIE First Grade College Department of Computer Science Karnataka Mysuru570008 India Saveetha School of Engineering SIMATS Department of Research and Innovation Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331505745

Vehicular Ad Hoc Networks (VANETs) are considered crucial for real-time vehicle-to-vehicle communication, which in turn enhances the efficiency of traffic and road safety. VANETs are very vulnerable to Denial-of-Service (DoS) attacks, which seriously disrupt communication and degrade network performance. This paper introduces a high-performance neural network-based detection and prevention mechanism that can effectively counter DoS attacks. The proposed method is initiated with the real-time monitoring of the network. Data collection and preprocessing through the Adaptive Self-Guided Loop Filter (ASGLF) are used to filter out noise from the data. The Fast Hybrid Vision Transformer (FHVT) conducts feature extraction for network traffic to effectively preserve the spatial and temporal relationships. Using the Reflecting Equivariant Quantum Neural Network (REQNN) integrated with Heterogeneous Edge-Enhanced Graph Attention Network (HEEGAT) ensures timely anomaly detection accuracy of attacks. Subtraction-Average-Based Optimizer (SAO) optimizes the detection parameters to effectively utilize resources. The experimental results have shown accuracy in the detection of DoS attacks as 99.9% with a PDR of 99.9% and also improved throughput efficiency by 99.8%. These results validate the effectiveness of the proposed model in securing VANETs from DoS attacks, offering uninterrupted network availability and improved vehicular communication security for intelligent transportation systems. © 2025 IEEE.

关键词： Advanced driver assistance systems

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Cloud Based Automated Control System Workshops and Rooms for Controlling Parameters 1

Cloud Based Automated Control System Workshops and Rooms for...

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1st international conference on Advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Kumar, Dinesh Kuntal, Ravinder Singh Deep, Pragya Chamoli, Anup Shekhar Singh, Pinki Mandal, Rishabh SRM Institute of Science & Technology Department of Computer Science and Engineering UP Ghaziabad India Nitte Meenakshi Institute of Technology Department of Mathematics Karnataka Bangalore India IIMT College of Engineering Department of AI & DS Greater Noida India Uttaranchal School of Computing Sciences Uttaranchal University Dehradun248001 India Accurate College of Engineering & Management Department of Computer Science and Engineering Greator Noida India Tula's Institute Department of Computer Science and Engineering UK Dehradun India

ISBN: (纸本)9798331518578

Across all industries, cloud-based automated control systems have completely changed how operational and environmental parameters are tracked and controlled. With cloud-integrated systems for real-time data collection, processing, and control, this concept presents intelligent, remote-controlled workshops and control rooms. To guarantee accurate control of crucial variables like temperature, humidity, pressure, and equipment efficiency, these systems make use of IoT devices, AI techniques, and big data analytics. Through sophisticated data perspectives, the cloud-based strategy enables predictive maintenance, improves flexibility, and lowers operating expenses. Stakeholders may make prompt, data-driven choices from anywhere with remote access to control rooms. These seminars provide practical experience with cloud-controlled settings and function as training and development centres. The system's flexibility to meet a range of industrial demands is further enhanced by its compatibility with external tools and equipment. The design, deployment, and possible uses of cloud-based control systems are covered in this paper, with an emphasis on how they might promote operational excellence, energy efficiency, and smart manufacturing. © 2025 IEEE.

关键词： Smart manufacturing

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Proposing ChatGPT as a Transformative Tool for Enhancing ESL Speaking and Listening Skills: Bridging Research Gaps in Thai Education 10

Proposing ChatGPT as a Transformative Tool for Enhancing ESL...

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10th international conference on Digital Arts, Media and technology and 8th ECTI Northern Section conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2025

作者： Dawilai, Sirikanya Yooyativong, Thongchai Kamyod, Chayapol Chiang Rai Rajabhat University Faculty of Humanities and Social Sciences Chiang Rai Thailand Mae Fah Luang University Center of Excellence in Artificial Intelligennce and Emerging Technology School of Applied Digital Technology Chiang Rai Thailand Mae Fah Luang University Computer and Communication Engineering for Capacity Building Research Center School of Applied Digital Technology Chiang Rai Thailand

ISBN: (纸本)9798331543273

The development of speaking and listening skills remains a significant challenge in English as a Second Language (ESL) education, particularly in non-native English-speaking contexts such as Thailand. Traditional tools and methods often fail to provide personalized, real-time conversational practice, leaving gaps in learners' communicative competence. This paper explores ChatGPT, a conversational AI, as a potential solution to these challenges. By conducting a comprehensive review of existing tools and identifying research gaps, this study proposes a conceptual framework for integrating ChatGPT into ESL education, particularly for Thai students at Chiang Rai Rajabhat University (CRRU). This framework highlights how ChatGPT's interactive capabilities can address cultural and linguistic barriers, enhance learner engagement, and provide scalable solutions for improving speaking and listening skills. The paper concludes with recommendations for future research and practical implications for educators. © 2025 IEEE.

关键词： Students

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TurfIt: An Instant Turf Booking Application

TurfIt: An Instant Turf Booking Application

引用

2025 IEEE international conference on Computational, Communication and Information technology, ICCCIT 2025

作者： Rajeswari, A.M. Tamilselvan, V. Abishek, M.R.P. Dept. of Computer Science and Engineering Velammal College of Engineering and Technology Madurai India

ISBN: (纸本)9798331512965

As sports grow in popularity need for effective online turf booking applications has also increased. Although some existing applications are faced with numerous challenges such as;restrictions on access, ineffectiveness in scheduling, and over-bookings, leading to frustrations and operational problems. This paper focuses on those problems by the presentation of an online turf booking application to streamline the booking process. The solution offers real-time updates, automatic payment, and safe transactions based on QR code. The more user-friendly the application is, the more efficient it will be. Firebase would be used for real-time data synchronization and the responsiveness of React interface ensures the smooth and conflict-free bookings. This solution would lead to increasing customer satisfaction as well as increasing the booking rates and effective utilization of sports facilities. © 2025 IEEE.

关键词： Customer satisfaction

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Early Prediction of Alzheimer's Dementia Using Random Forest Machine Learning Algorithm: A Data-Driven Approach 8

Early Prediction of Alzheimer's Dementia Using Random Forest...

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8th international conference on Electronics, Materials engineering and Nano-technology, IEMENTech 2025

作者： Vani Shree, K. Pradeep, K. Rambabu, Bandi Surendran, R. Balamurugan, K.S. Karpaga Vinayaga College of Engineering and Technology Department of Computer Science Engineering Tamil Nadu Chengalpattu India Chennai Institute of Technology Department of Biomedical Engineering Chennai India CVR College of Engineering Department of Computer Science Engineering Hyderabad India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Karpaga Vinayaga College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Chengalpattu India

ISBN: (纸本)9798331520762

Alzheimer's dementia is liberal neurological ailment categorized by memory loss, cerebral failure, and it is the utmost communal form of dementia and typically affects older adults. Every year, 6.7 million people are affecting by Alzheimer's dementia (AD) in USA and 4.3% probability of developing Alzheimer's ailment earlier the age of 65 in India. The principal issue associated with delayed prediction of Alzheimer's dementia lies in the incapacity to intervene during the formative years of the dementia progression, thereby missing opportunities for timely intervention and treatment. Available techniques for finding of Alzheimer's dementia with machine learning algorithms resulted in inability to predict the dementia early. For proactive diagnosis of Alzheimer's dementia, proposed a novel technique using Random Forest categorizer and also compared among various machine learning classifiers. Resulted in obtained accurate results with 97.5% of accuracy. © 2025 IEEE.

关键词： Random forests

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