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检索条件"任意字段=3rd International Conference on Intelligent Data Engineering and Automated Learning"

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Big data-Driven intelligent Structure of Two-ticket System for Safety Production in Power Grid Enterprise 3

Big Data-Driven Intelligent Structure of Two-ticket System f...

引用

3rd international conference on Electronics and Information Technology, EIT 2024

作者： Huang, Chengwei Yang, Jian Xie, Wenhai Han, Lei Deng, Yan Hezhou Power Supply Bureau of Guangxi Power Grid Co. Ltd. Guangxi China Hohai University College of Information Science and Engineering Changzhou China

ISBN: (纸本)9798350369151

As power systems become more intelligent, the intelligent structure of two-ticket system in power grids is key to improving efficiency and safety. This paper investigates the integration of edge computing, artificial intelligence, IoT, and big data into a three-layer system: data collection, edge computing, and central management. Each layer enhances real-time performance, reliability, and intelligence. The system reduces latency through local processing and optimizes decision-making at the central level. Application scenarios demonstrate its practicality and potential for improving power grid operations. © 2024 IEEE.

关键词： Smart power grids

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An Exploratory Study of dataset and Model Management in Open Source Machine learning Applications 24

An Exploratory Study of Dataset and Model Management in Open...

引用

IEEE/ACM 3rd international conference on AI engineering - Software engineering for AI (CAIN)

作者： Toma, Tajkia Rahman Bezemer, Cor-Paul Univ Alberta Edmonton AB Canada

ISBN: (纸本)9798400705915

datasets and models are two key artifacts in machine learning (ML) applications. Although there exist tools to support dataset and model developers in managing ML artifacts, little is known about how these datasets and models are integrated into ML applications. In this paper, we study how datasets and models in ML applications are managed. In particular, we focus on how these artifacts are stored and versioned alongside the applications. After analyzing 93 repositories, we identified the most common storage location to store datasets and models is the file system, which causes availability issues. Notably, large data and model files, exceeding approximately 60 MB, are stored exclusively in remote storage and downloaded as needed. Most of the datasets and models lack proper integration with the version control system, posing potential traceability and reproducibility issues. Additionally, although datasets and models are likely to evolve during the application development, they are rarely updated in application repositories.

关键词： Machine learning (ML) artifact management ML application development SE4AI

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Design of Smart Tourism Visual Analysis Platform Based on Digital Twin 3

Design of Smart Tourism Visual Analysis Platform Based on Di...

引用

3rd IEEE international conference on Electrical engineering, Big data and Algorithms, EEBDA 2024

作者： Li, Suiqun Feng, Yidan Chu, Qikai Chen, Guangjian Sichuan University of Science and Engineering Sichuan Key Provincial Research Base of Intelligent Tourism Zigong China School of Computer Science Sichuan University of Science and Engineering Zigong China

ISBN: (纸本)9798350380989

With the continuous development and application of new technologies such as big data, Internet of Things, cloud computing, virtual reality and artificial intelligence, intelligent tourism has ushered in new development opportunities. More and more cities begin to pay attention to the information construction of scenic spots, and look forward to new technologies to provide scientific and effective analytical data for the tourism industry. In view of the above problems, the smart tourism visual analysis platform based on digital twins is designed to conduct a comprehensive and detailed analysis of the tourist destination's traffic situation, security supervision, resource information, weather environment monitoring and epidemic prevention and control through the method of data visualization and visual analysis. The results indicate that the platform presents information to users in a digital twin and visualized manner, with characteristics such as scientific information management and intelligent processing and analysis. © 2024 IEEE.

关键词： Digital twin Smart tourism Visual analysis Visualization

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Robust Federated learning Based on Chained Self-Paced learning 3

Robust Federated Learning Based on Chained Self-Paced Learni...

引用

3rd international conference on Computer Science, Electronic Information engineering and intelligent Control Technology, CEI 2023

作者： Jia, Yanan Li, Wenxin Yan, Mengwei Yan, Bowen School of Computer and Artificial Intelligence Beijing Technology and Business University Beijing China

ISBN: (纸本)9798350372052

Federated learning is a framework that enables distributed collaborative training while preserving data privacy, and it has great potential for enhancing diagnostics in the medical field. Due to the reluctance of participating institutions to disclose or share details of their models, they design their own network architectures. However, this heterogeneity in models makes it challenging to ensure that all client-collected data in federated learning is correctly annotated, inevitably leading to noise. Therefore, we propose a spiral self-training strategy to address the issue of internal client noise, combating corrupted label noise through reweighted aggregation updates across different time periods. Our proposed federated learning approach improves the original baseline by 5.30% under a noise rate of 0.1 and the pairflip noise scheme. Under symflip noise, it achieves a 5.87% improvement. Similarly, at a noise rate of 0.2, there is a substantial improvement. Extensive experiments demonstrate that our method significantly outperforms recent state-of-the-art methods on noisy label data. © 2023 IEEE.

关键词： Network architecture

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Cyberbullying Prediction using K-Means Clustering with learning Methodologies for Emotion Detection in Tweets 3

Cyberbullying Prediction using K-Means Clustering with Learn...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Privietha, P. Sasirekha, R. Melwin Akash, L. Punitha, K. Marshal Felix, A. Ramyabharathi, R. Department of CSE Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798331527549

The rise of social media has led to an increase in real-time data that can reveal potential cyber threats. This research explores the use of emotion detection in tweets to predict cyber attacks, employing K-Means Clustering in combination with machine learning methodologies. By analyzing emotional trends, such as fear, anger, and anxiety, this study establishes a correlation between social media sentiment and cyber attack occurrences. The results demonstrate the potential for early warning systems that harness emotional patterns for cybersecurity. Social media platforms like Twitter provide a wealth of real-time data that can reveal societal trends, including indicators of potential cyber threats. This study investigates the use of emotion detection in tweets as a predictive tool for cyber attack forecasting. By employing Natural Language Processing (NLP) techniques, emotional states such as fear, anger, and anxiety are extracted and analyzed. K-Means Clustering is utilized to group similar emotional patterns, enabling the identification of significant trends. These clusters, combined with historical cyberbullying data, are used as features in supervised learning models for cyber threat prediction. The results demonstrate the effectiveness of integrating unsupervised clustering and machine learning to correlate spikes in negative emotions with the likelihood of cyber incidents. This research highlights the potential for proactive cybersecurity strategies that leverage social media sentiment as an early warning system. © 2025 IEEE.

关键词： Tweets

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A Deep-learning Approach for the Identification of New Subtypes of Lung Cancer 25th

A Deep-Learning Approach for the Identification of New Subty...

引用

25th international conference on intelligent data engineering and automated learning

作者： Banerjee, Tuhin Corradini, Andrea Canon EMEA Amstelveen Netherlands MCI Innsbruck Austria

ISBN: (纸本)9783031777301;9783031777318

Lung cancer is the deadliest cancer in the world. It is caused by unchecked cell division of damaged cells in the lungs forming tumors that eventually prevent the lung from functioning properly. Identification of novel unsupervised subtypes of lung cancer is critical to reveal new insights into the underlying biology of cancer and ensure that patients receive specialized precision treatment based on the subtype of cancer they are suffering from. The ability of modern sequencing tools to produce patient-specific RNA sequencing (RNA-seq) gene expression data has transformed cancer research by offering in-depth understanding of the molecular landscape of cancer. This paper reports on a pipeline that comprise of a Deep Autoencoder (DAE) model coupled with hierarchical agglomerative clustering (H-Clust). It aims to identify new unsupervised lung cancer subtypes from RNA-seq expression samples collected from a publicly available dataset. Further, a deep learning (DL) model, Artificial Neural Network (ANN) is used to classify a patient's data into one of the newly identified subtypes.

关键词： lung cancer subtype TCGA RNA-seq deep learning DAE encoder-decoder agglomerative clustering ANN

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Modern Techniques of Power System Analysis with intelligent System 3

Modern Techniques of Power System Analysis with Intelligent ...

引用

3rd international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2023

作者： Kadam, Sandeep Ratnadeep Mulla, A.M. Shivaji University Department of Technology Maharashtra Kolhapur India Daulatrao Aher College of Engineering Maharashtra Karad India

ISBN: (纸本)9781665494007

Analysis of power system consisting of analysis of power distribution, power transmission and power generation. It is very important for efficient operation of power system. Different modern techniques are used for power system analysis by using intelligent system, artificial intelligence, machine learning and optimization techniques. intelligent system is useful for different businesses and industries. While analyzing the power system, different parameters are considered such as arc resistance, fault current and fault voltage. Analysis of power system with modern intelligent electronic devices, distributed generation and hybrid type superconducting fault current limiter is very important. While calculating fault current and arc resistance for different fault conditions in power system, business intelligence tool is used for data integration, data analysis and data visualization. © 2023 IEEE.

关键词： intelligent systems

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Research on SF6 Leakage Detection Based on Artificial Intelligence Gas Cloud Leakage Algorithm 3

Research on SF6 Leakage Detection Based on Artificial Intell...

引用

3rd international conference on Robotics, Artificial Intelligence and intelligent Control (RAIIC)

作者： Lai, Junju Xie, Da Lai, Jiawen Wang, Jingcheng Xian, Feng Chen, Yunxin Guangdong Power Grid Co Ltd Qingyuan Power Supply Bur Qingyuan 511500 Guangdong Peoples R China

ISBN: (纸本)9798350352634;9798350352627

Sulfur hexafluoride (SF6) gas is extensively used in high-voltage equipment such as transformers and circuit breakers due to its excellent insulation and arc-extinguishing properties. However, SF6 gas leakage poses significant safety and environmental risks. This study aims to develop an AI-based gas cloud leakage detection algorithm that utilizes infrared imaging data and deep neural networks for real-time detection and localization of SF6 gas leaks. The proposed method integrates infrared imaging technology with advanced deep learning algorithms to enhance detection accuracy and sensitivity, reducing the need for manual intervention. Experimental results show that the system achieves an overall detection accuracy of 82.71%, with a missed detection rate of 13.88% and a false alarm rate of 3.41%. This approach ensures the safety of equipment and personnel, and mitigates environmental impact, providing a reliable and efficient solution for SF6 leakage detection in the power industry.

关键词： SF6 gas leakage,Iinfrared imaging Deep learning Gas cloud detection

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Research on Collaborative Optimization of Big data Analysis and Artificial Intelligence Based on Deep learning 24

Research on Collaborative Optimization of Big Data Analysis ...

引用

3rd international conference on Frontiers of Artificial Intelligence and Machine learning (FAIML)

作者： Zhang, Xu Li, Suruo Chen, Huafeng Jingchu Univ Technol Sch Comp Engn Jingmen 448000 Peoples R China Jingchu Univ Technol Big Data Res Ctr Jingmen 448000 Peoples R China

ISBN: (纸本)9798400709777

Drawing upon deep learning techniques, this study investigates big data analysis methods across various scenarios to enhance data value. The proposed intelligent collection device is capable of harvesting both unstructured data, such as videos and images, and structured data, including geographical coordinates, operational metrics, and electronic device identifiers. Building on this, the paper introduces an architecture for a big data analysis system underpinned by deep learning, elaborating on its critical technologies. Furthermore, it underscores how community and end-to-end power transactions bolster the adaptability and maximize the value of power generation entities. Although Multi-Agent Deep Reinforcement learning offers a novel approach to managing energy among multiple prosumers, challenges such as environmental volatility, the safeguarding of prosumer privacy, and computational demands persist. This research aims to explore a multi-agent reinforcement learning algorithm that employs parameter sharing and deep deterministic policy gradients to enhance learning efficiency and mitigate training complexities through shared strategies and experiences among agents. Additionally, by leveraging a reputable third party for disseminating comprehensive community market data to prosumers, this approach not only promises to secure prosumer privacy effectively but also to minimize environmental uncertainty and augment the algorithm's scalability.

关键词： Deep learning Big data analysis Artificial intelligence collaborative optimization

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Forecasting Stock Market Volatility Using XGBoost: A Time Series Analysis 3

Forecasting Stock Market Volatility Using XGBoost: A Time Se...

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3rd international conference on Communication, Control, and intelligent Systems, CCIS 2024

作者： Sharma, Tripti Prasad, Sanjeev Kumar Prasad, Shreeya Verma, Indu Sharma, Anupama Ghaziabad India School of Computer Science Engineering Galgotias University Greater Noida India Ajay Kumar Garg Engineering College Department of Computer Science and Engineering Ghaziabad India University Delhi-NCR India Ajay Kumar Garg Engineering College Department of Information Technology Ghaziabad India

ISBN: (纸本)9798331528201

Forecasting stock market volatility presents significant challenges and opportunities for both practitioners and researchers in the financial sector. This paper explores the application of eXtreme Gradient Boosting (XGBoost), an advanced ensemble machine learning technique, to predict stock market volatility using time series data from the National Stock Exchange of India (NSEI). Our methodology utilizes past price data containing open, high, low, and close prices, along with capacity and adjusted close values, to capture the underlying patterns indicative of future market movements. We preprocess this data using techniques like normalization and feature engineering to enhance the predictive capabilities of our model. The XGBoost model is upgraded through extensive hyperparameter tuning and is considered in contrast to customary measurements like Mean Squared Error (MSE), Root Mean Squared Blunder (RMSE), and the R2 Score to survey its precision and adequacy. Our results demonstrate that XGBoost can effectively capture the complexities and non-linear relationships inherent in stock price movements, outperforming standard benchmark models. The findings suggest that machine learning models, particularly those based on tree ensemble methods like XGBoost, provide a robust tool for financial analysts seeking to mitigate risks and capture opportunities in volatile markets. This paper contributes to the ongoing discourse in financial econometrics by confirming the viability of advanced machine learning techniques in forecasting market dynamics, thus offering valuable insights for both theoretical advancement and practical application in financial forecasting. © 2024 IEEE.

关键词： Time series

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