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

共 438191 条记录，以下是4981-4990 订阅

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Effective Integration of Innovative computer Vision Tools into Electronics Manufacturing Process

Effective Integration of Innovative Computer Vision Tools in...

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international Youth conference on Radio Electronics, Electrical and Power engineering (REEPE)

作者： Arina A. Adamova Andrey I. Vlasov Elena A. Medvedeva Department of Design and Production Technology of Electronic Equipment Bauman Moscow State Technical University Moscow Russian Federation

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

The success and accuracy of analysis and processing of raster images of electronic assemblies entering the machine vision algorithms and models within the electronics manufacturing process depend on parameters of these images. This necessitates the development of recording modules for computer vision systems using multifunctional cameras that allow not only capturing but also the maximum possible pre-processing of images. The paper analyzes the processing of raster images using machine vision methods and formulates the key requirements of computer vision algorithms and models for input images. A structural diagram of a computer vision system is presented. In accordance with this diagram, a recording module based on a 2-megapixel camera has been developed. Physical modeling of the module operation has been carried out using the example of processing an image of an electronic assembly. The proposed software and hardware version of the recording module solves the problem of effective implementation of innovative machine vision methods in electronics manufacturing process by pre-processing images on the camera side and their visualization in order to control the success of the system reconfiguration.

关键词： computer vision Manufacturing processes Machine vision Computational modeling Production Cameras Hardware Recording Consumer electronics Assembly

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MICAN: Multi-modal Inconsistency-Based Cooperation Attention Network for Fake News Detection 31st

MICAN: Multi-modal Inconsistency-Based Cooperation Attention...

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31st international conference on Multimedia Modeling, MMM 2025

作者： Yi, Zepu Lu, Songfeng Tang, Xueming Zhu, Jianxin Wu, Junjun School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen China

ISBN: (纸本)9789819620609

With the popularity of social media and online platforms, fake news has spread faster and more widely than ever before, with far-reaching impacts on many fields such as politics, economics, and society. Therefore, the research and development of fake news detection technology is particularly important. At present, the research in the field of fake news detection mainly focuses on the fusion and consistency detection of images and texts, and remarkable results have been achieved in this direction. However, the existing methods still face two major challenges: first, how to integrate multimodal features more evenly to ensure that the image and text are equal in the model;The second is how to construct a fusion model that can effectively deal with the potential inconsistencies between images and texts. In order to solve the above challenges, this paper proposes a Collaborative Attention Network Based on Multimodal Inconsistency (MICAN) for fake news detection. MICAN can not only fuse the features of text and images, but also effectively solve the problem of image-text mismatch that may occur during the fusion process. Through the innovative multi-modal collaborative attention network framework and mutual attention neural network architecture, the network realizes the accurate fusion of multimodal features, and enhances the accuracy and robustness of fake news detection. In addition, the MICAN model also adds an event discriminator, which is responsible for predicting the labels of events to achieve accurate classification and identification of news events, which further improves the practicability of the model. Experimental results show that the MICAN model outperforms the existing fake news detection models on multiple datasets such as weibo, twitter, and PHEME, which verifies its effectiveness and superiority. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Co-attention Fake news detection Inconsistent reasoning Multimodal fusion

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Advanced Feature engineering and Model Improvement for Sarcasm Detection Using Deep Learning Architectures

Advanced Feature Engineering and Model Improvement for Sarca...

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IEEE Students' conference on Electrical, Electronics and computer Science (SCEECS)

作者： Manish Chandra Roy Sukant Kishoro Bisoy Pradipta Das Depatment of Computer Science & Enginnering C.V. Raman Global Universtiy Bhubaneswar Odisha India Department of Information Technology Veer Surendra Sai University of Technology Burla Odisha India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

Sarcasm detection, crucial for enhancing sentiment analysis and human-computer interaction, poses challenges due to its nuanced, context-dependent nature. This study investigates advanced feature engineering and model enhancement techniques to improve sarcasm detection using deep learning. Traditional models often falter with sarcasm's complexities, so we propose a hybrid approach combining contextual embeddings, syntactic patterns, and sentiment-aware features. We test deep learning models like LSTM, GRU, and Transformer-based architectures, incorporating attention mechanisms and pre-trained models like BERT for focused analysis. Our method uses sarcasm-specific cues, with data preprocessing and augmentation. We employ Recursive Feature Elimination (RFE) and PCA to optimize feature space, and evaluate on public sarcasm datasets. Results show improved accuracy and robustness, highlighting applications in social media analysis, customer feedback, and content moderation.

关键词： Deep learning Analytical models Sentiment analysis Attention mechanisms Social networking (online) computer architecture Syntactics Feature extraction Transformers Robustness

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Exploring Consumer Behavior in E-Commerce with AI Personalization and Market Trends Analysis Models

Exploring Consumer Behavior in E-Commerce with AI Personaliz...

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Integrated Circuits and Communication Systems (ICICACS), IEEE international conference on

作者： Gaurav Kumar G. Devayani C Saraswathi R. Melba Kani Sandeep Dongre M. S. Mohamed Mallick School of Computer Application Lovely Professional University Phagwara India Department of Computer Science and Business Systems M.Kumarasamy College of Engineering Karur India School of Management Studies Bannari Amman Institute of Technology Erode India Master of Business Administration Mar Ephraem college of Engineering and Technology Kanyakumari India Symbiosis Institute of Business Management (SIBM) Constituent of Symbiosis International (Deemed University) Nagpur India Department of Computer Science Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology (Deemed to be University) Chennai India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Online retailers must monitor and respond to changing consumer trends to stay competitive. AI-driven customization's revolutionary impact on market trends is the focus of this study on online buying and customer behavior. As AI advances, e-commerce platforms will be able to customize user experiences. Preprocessing, feature extraction, and model training comprise the AI-driven personalization process. Customer data from online marketplaces is collected first. It manages missing values, balance the data, encode categorical variables, scale the features, and minimize the dimensions as part of our thorough data preprocessing to ensure fair and applicable results. To trained the algorithms with CNN and Convolutional LSTM Based on key criteria, the AI model beat LSTM and CNN. It provides personalized insights and recommendations with an average accuracy of 92.03%. Personalized experiences and product suggestions enabled by AI-powered personalization have transformed e-commerce. It's important to use smart algorithms to engage customers and examine large databases. This proposed approach to shows that AI can transform online shopping and improve customer experiences.

关键词： Consumer behavior Accuracy Data preprocessing Transforms Market research Electronic commerce Convolutional neural networks Integrated circuit modeling Artificial intelligence Long short term memory

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Comparative Analysis of ML Algorithms for Forecasting Time Series Stock Returns

Comparative Analysis of ML Algorithms for Forecasting Time S...

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Sentiment Analysis and Deep Learning (ICSADL), international conference on

作者： Bala Karthikeya Singh Bondili Sai Pavan Bolagani Rakshith Thatikonda Computer Science and Engineering VNR Vignana Jyothi Institute of Engineering and Technology Telangana India Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andra Pradesh India Electronics and Communication Engineering CMR Technical Campus Telangana India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Forecasting stock returns is a challenging task due to high data variability, noise, and the inherent unpredictability of financial markets. Traditional methods like ARIMA and regression lack scalability and fail to capture complex data patterns. Recent advancements in machine learning, including ensemble methods such as Random Forest and Boosting, have shown promise in improving predictive accuracy. However, these methods face challenges such as overfitting, computational complexity, and data dependency. This paper evaluates the performance of various machine learning algorithms and their ability to address these challenges to improve forecast reliability. By using techniques like dimensionality reduction and hyperparameter tuning, the study identifies strategies to enhance prediction accuracy while reducing computational complexity and costs.

关键词： Dimensionality reduction Time series analysis Predictive models Boosting Ensemble learning Forecasting Computational complexity Tuning Random forests Principal component analysis

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Solid Waste Management (SWM) Dashboard: Design and Development of Smart Sanitation Solution for Rural Areas

Solid Waste Management (SWM) Dashboard: Design and Developme...

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international conference on ent Computing and Control Systems (ICICCS)

作者： Minal Patel Kashyap Patel Akash Mehta Priyam Mehta Akash Patel Computer Engineering Department Sardar Vallabhbhai Patel Institute of Technology (GTU) Anand India Computer Engineering Department Devang Patel Institute of Advance Technology and Research CHARUSAT Anand India Gujarat Technological University (GTU) Ahemdabad India Information Technology Department Sardar Vallabhbhai Patel Institute of Technology (GTU) Anand India Information Technology Department Devang Patel Institute of Advance Technology and Research CHARUSAT Anand India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

The paper aims to address the challenges of waste management in rural areas by designing and developing a smart sanitation solution. The proposed dashboard provides real-time information on the waste collection status of SWM workers, monitors the status of major bins in villages, and facilitates decision-making for the composting process. The dashboard also allows villagers to engage with the waste management system, stay updated on its status, and report any issues related to waste management in their area. Additionally, the SMW supervisor can use the dashboard to monitor waste collection details, address ongoing waste-related issues, and ensure the timely completion of the composting process. The study of this paper presents the design of smart dashboard which is integrated with IoT-based monitoring and waste management. By discussing innovative methods, the proposed dashboard system ensures efficient solid waste management with scalability for the conditions found in rural areas. In this paper it seems to emphasize the vision for clean and sustainable villages, highlighting the importance of practical and sensible decision-making for waste collection in achieving this goal.

关键词： Waste management Waste materials Cloud computing Composting Smart cities Scalability Decision making Machine learning Internet of Things Monitoring

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Optimal Power Dispatch in Combined Heat and Power Systems with Solar and Wind Integration

Optimal Power Dispatch in Combined Heat and Power Systems wi...

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international conference on computer, Electrical & Communication engineering (ICCECE)

作者： Roshan Ghosh Koustav Dasgupta Sakti Prasad Ghoshal Department of Electrical Engineering Budge Budge Institute of Technology Kolkata India Department of Electrical Engineering Techno India University Kolkata India

ISBN: (数字)9798331513894

ISBN: (纸本)9798331513900

Cogeneration, or combined heat and power (CHP) generation system delivers electrical energy and recovers dissipated heat, reducing costs, emissions, and enhancing renewable energy integration by smoothing intermittency. CHP economic dispatch problem (CHPED) is necessary for handling load dynamics and addressing the uncertainty of nonconventional energy sources. The primary focus of this proposed work is to minimize overall operational expenses while adhering to generator constraints and giving priority to solar, wind power output. For solving this CHPED problem, an arithmetic optimization algorithm (AOA) is utilized to obtain an optimal solution. In this study, a 10 -unit framework is tested to establish the effectiveness of CHPED both including and excluding solar and wind power. Obtained results shows significant decrease of $18 \%$ in operational costs by incorporating considered renewable energy sources. This approach can enhance the integration of wind and solar source energy generated power as distributed sustainable energy into the CHPED process.

关键词： Renewable energy sources Costs Smoothing methods Wind energy Cogeneration Wind power generation Power system stability Optimization Heat recovery Resistance heating

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Explaining the Design of the Quantum Fourier Transform 20th

Explaining the Design of the Quantum Fourier Transform

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20th international conference on Grid, Cloud, and Cluster Computing, GCC 2024, 3rd international conference on Quantum Technologies, ICEQT 2024, and 21st international conference on Modeling, Simulation and Visualization Methods, MSV 2024, held as part of the World Congress in computer Science, computer engineering and Applied Computing, CSCE 2024

作者： Deligiannidis, Leonidas Wentworth Institute of Technology BostonMA02115 United States

ISBN: (纸本)9783031858833

The Discrete Fourier Transform (DFT) is one of the most practical mathematical tools that has been developed and is used in engineering, sciences and beyond. It converts a finite sequence of samples from the time/space domain into the frequency domain, and vice versa. Many operations and analyses are simpler in the frequency domain where after the appropriate operation (or filtering), the signal is converted back to the spatial domain. The Quantum Fourier Transform (QFT) is the quantum equivalent of the classical DFT. Instead of operating on classical signals, it operates on quantum states. Many important quantum algorithms utilize the QFT, or the inverse QFT, at their last step to extract the periodicity from their input quantum states. Some of these algorithms are Shor’s, Quantum Phase Estimation (QPE), Quantum Counting and numerous other algorithms. In this work, we attempt to present QFT in a way that undergraduate students can understand the design of the algorithm and the reasoning of the implementation. We wish to utilize this work to develop a tutorial to understand the intricacies of QFT’s implementation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Frequency domain analysis

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CNN based Optimized Autoencoder for Satellite Image Compression

CNN based Optimized Autoencoder for Satellite Image Compress...

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Interdisciplinary Approaches in technology and Management for Social Innovation (IATMSI), IEEE conference on

作者： P. Sowmya Sri S. Karthi M. Kalaiyarasi S. Saravanan Computer Science and Engineering (Data Science) B V Raju Institute of Technology Narsapur Telangana India Department of Computer Science and Engineering V.S.B.Engineering College India Institute of AI & ML Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai India Department of EEE B V Raju Institute of Technology Telangana India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

This paper introduces an innovative Convolutional Neural Network (CNN) based Optimized Autoencoder for the compression of satellite images, with a focus on high resolution Sentinel-2 and Landsat imagery. In this method, the Satellite images are resized and normalized before an optimal autoencoder architecture is implemented. The encoder compresses the image, while the decoder reconstructs it with minimum quality loss. Convolutional and down sampling layers in the encoder and up sampling layers in the decoder preserve image features. MSE, PSNR, SSIM, and CR were used to assess compression performance. The simulation outcomes show that the CNN-based Autoencoder can retain image quality across images with a high compression ratio of 512, an average MSE of 0.0064, an average PSNR of 25.214 dB, and an SSIM of 0.62. This satellite image compression approach is efficient enough for storage and transmission in satellite data applications.

关键词： Image quality Technological innovation Image coding Satellites Autoencoders Memory Real-time systems Satellite images Decoding Convolutional neural networks

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Smart Vehicle Procurement System Using Blockchain technology

Smart Vehicle Procurement System Using Blockchain Technology

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Boddu Manoj Kumar G. Rajeswari Kavitha Duraipandian Shiela David Dept. of Computer Science and Engineering with specialization in Blockchain Technology Sathyabama Institute of Science and Technology Chennai Tamil Nadu Dept. of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Blockchain technology has become more widespread in our lives revolutionizing various industries through improved security, transparency, and operational efficiency. Its decentralized ledger system is particularly effective in areas like finance and supply chain management ensuring data integrity and reducing the need for middlemen. This study explores how blockchain technology is used in the process of buying vehicles highlighting its benefits compared to methods. Traditional vehicle purchases involve a network of dealers, banks and regulators leading to inefficiencies, higher costs and potential fraud. The usual process involves steps like financing checking vehicle history and changing ownership. Each requiring extensive paperwork and third-party intervention. In contrast using blockchain for vehicle purchases can simplify things through contracts that automate tasks like verifying history securely transferring ownership instantly settling payments. The unchangeable nature of records ensures that all transaction data remains secure against tampering fostering trust between buyers and sellers. Furthermore, blockchain can cut costs significantly by removing the need for middlemen such, as escrow services and title companies while reducing burdens. The advanced security elements built into technology help reduce the chances of fraudulent actions like altering odometer readings or selling stolen cars. This study showcases a comparison illustrating how blockchain can simplify the process of buying vehicles offering an economical and secure experience.

关键词： Industries Costs Smart contracts Blockchains Fraud Security History Reliability Standards Automotive engineering

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