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

共 438190 条记录，以下是4991-5000 订阅

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Industrial IoT Smart Manufacturing Systems based on Cloud Computing Energy Efficiency Analysis using Machine Learning Algorithms

Industrial IoT Smart Manufacturing Systems based on Cloud Co...

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Electronics and Renewable Systems (ICEARS), international conference on

作者： P. Chinnasamy Patan Feridoz Khan C Sasikala Mavalluru Swathi M Prema Kumar Ajay Kumar Yadav Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamilnadu India Department of Computer Science and Engineering School of Computing Mohan Babu University Tirupati Andhra Pradesh India Dept of CSE Srinivasa Ramanujan Institute of Technology (Autonomous) Ananthapuramu Andhra Pradesh India Audisankara College of Engineering and Technology Andhra Pradesh India Dept. of ECE Shri Vishnu Engineering College for Women (A) Bhimavaram Andhra Pradesh India Department of Computer Application United Institute of Management Prayagraj India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

With open connectivity and emergent computing, the Industrial Internet of Things (IIoT) combined with cloud computing resources provides significant breakthroughs in the field of industrial automation. These developments lead to the evolution of supplemental services, which improve automation in the production and industrial sectors. This research proposes novel technique in smart manufacturing system based on cloud computing with IIoT in energy efficiency using machine learning. Here the smart manufacturing process has been analysed based on IIoT with cloud computing network, where the energy efficiency is analysed using virtual machine-based reinforcement markov chain based linear regression. Experimental analysis has been carried out in terms of latency, energy efficiency, task response time, accuracy. proposed technique obtained 97% of Latency, 95% of Energy efficiency, 98% of Accuracy, 94% of Task response time.

关键词： Cloud computing Machine learning algorithms Automation Accuracy Linear regression Energy efficiency Virtual machines Time factors Industrial Internet of Things Smart manufacturing

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Towards Precision Cardiovascular Medicine - A Novel Framework for Risk Prediction Using MultiOmics and Imaging Data

Towards Precision Cardiovascular Medicine - A Novel Framewor...

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Automation and Computation (AUTOCOM), international conference on

作者： S Lalitha V. Sujay A.P. Jaya Krishna S Navaneethan Abijith G R S. Sugantha Priya Department of CSE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Krishna University College of Engineering and Technology Machilipatnam Krishna Andhra Pradesh India Department of Electronics and Communication Engineering Sona College of Technology Salem Tamilnadu India Department of Electronics and Communication Engineering Saveetha Engineering College Chennai Tamil Nadu India Department of Information Technology St.Joseph's Institute of Technology Chennai Tamil Nadu India Department of Computer Science Dr SNS Rajalakshmi College of Arts and Science Coimbatore Tamil Nadu India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

The framework integrated multi-omics data and imaging characteristics to boost predictive precision for cardiovascular risk. Existing studies chiefly relied on restricted clinical information using common logistic regression designs unable to capture cardiovascular diseases (CVD's) intricacy. Such dated techniques experienced diminished accuracy and lack personalized risk prediction. But these restrictions are surpassed by the suggested system since it uses deep learning algorithms such as Random Forest, Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) for structured data and visual information. To prepare the data, standardization and simplification of complexity firstly applied preprocessing techniques such as PCA and t-SNE first, before utilizing feature selection via RFE and LASSO. Results demonstrated substantially improved outcomes with 0.85 accuracy, 0.82 sensitivity and an AUC-ROC of 0.90, delivering a more trustworthy tool for early intervention and customized cardiovascular care. Complex sentences mixed with shorter, simpler constructions to vary structure and keep readers engaged throughout the lengthier description.

关键词： Deep learning Recurrent neural networks Accuracy Computational modeling Predictive models Data models Complexity theory Convolutional neural networks Cardiovascular diseases Biomedical imaging

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Towards Precision Cardiovascular Medicine - A Novel Framework for Risk Prediction Using MultiOmics and Imaging Data

Towards Precision Cardiovascular Medicine - A Novel Framewor...

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3rd IEEE international conference on Automation and Computation, AUTOCOM 2025

作者： Lalitha, S. Sujay, V. Krishna, A.P. Jaya Navaneethan, S. Abijith, G.R. Priya, S. Sugantha Department of Cse Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Tamil Nadu Chennai India Krishna University College of Engineering and Technology Department of Computer Science and Engineering Krishna Andhra Pradesh Machilipatnam India Sona College of Technology Department of Electronics and Communication Engineering TamilNadu Salem India Saveetha Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Department of Information Technology St.Joseph's Institute of Technology Tamil Nadu Chennai India Department of Computer Science Dr Sns Rajalakshmi College of Arts and Science Tamil Nadu Coimbatore India

ISBN: (纸本)9798331542375

关键词： Early Intervention Metabolomics Multi-Omics Integration Precision Cardiovascular Medicine Proteomics

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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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A Review on Meta-Learning: How Artificial Intelligence and Machine Learning Can Learn to Adapt Quickly

A Review on Meta-Learning: How Artificial Intelligence and M...

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Electronics and Renewable Systems (ICEARS), international conference on

作者： Shrikant Khadse Palash Gourshettiwar Adesh Pawar Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (Deemed to be University) Wardha India Department of Computer Science and Medical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha India Department of Computer Science and Medical Engineering Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Meta-learning aims to create Artificial Intelligence (AI) systems that can adapt to new tasks and improve their performance over time without extensive retraining. The advent of meta-learning paradigms has fundamentally changed how Artificial Intelligence (AI) and Machine Learning (ML) are handled, constantly reshaping these fields. Research study reviews the broad field of meta-learning, highlighting its critical function in bridging the AI-ML divide to improve adaptability. The study starts with a review of past developments, following the development of meta-learning algorithms and their underpinning theories. The historical view lays the groundwork for understanding the course of this dynamic regulation, spanning from early efforts in probabilistic program induction to modern discoveries in model-agnostic meta-learning. The study explores multiple meta-learning applications in AI and ML, revealing how they affect financial forecasting, computer vision, natural language processing, autonomous cars, health informatics, robotics, and personalized recommendation systems. When seen from the perspective of adaptive intelligence, meta-learning may be effectively applied to conditions with limited data, hyperparameter optimization, few-shot learning, and quick adaption in reinforcement learning settings.

关键词： Metalearning Adaptation models Renewable energy sources Reviews Shape Reinforcement learning Benchmark testing Regulation Artificial intelligence Recommender systems

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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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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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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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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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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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