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Citation Based Scientific Document Summarization Using Deep learning 4th

Citation Based Scientific Document Summarization Using Deep ...

引用

4th International conference on Advanced Network Technologies and intelligent Computing-ANTIC-Annual

作者： Jyoti, Divya Srivastava, Jyoti Mahato, Dharmendra Prasad Natl Inst Technol Hamirpur Dept Comp Sci & Engn Hamirpur 177005 Himachal Prades India

ISBN: (纸本)9783031837920;9783031837937

With the rapid increase in the volume of scientific literature, researchers face challenges in keeping up with the latest advancements while summarizing the documents. Scientific document text summarization offers a solution by providing concise and informative summaries that highlight the key contributions from original texts. this study introduces a novel method leveraging deep learning, specifically the sBERT model to summarize scientific documents. the proposed approach treats the extractive summarization as a classification problem using a dual BERT model setup. the methodology is evaluated using data set from CL-SciSumm. Results indicate that our approach significantly outperforms the existing methods in terms of ROUGE scores, demonstrating its effectiveness in generating accurate summaries of scientific literature.

关键词： Citation Based Summarization Scientific Documentation Deep learning BERT and sBERT model Natural Language Processing

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Deep Reinforcement learning-based Real-time Online Energy Management Strategy for Electric Vehicle Charging Stations 4

Deep Reinforcement Learning-based Real-time Online Energy Ma...

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4th Power System and Green Energy conference (PSGEC)

作者： Yang, Zhaoqiang Li, Longtan Yao, Rui Liu, Chunxiu Liu, Yimin Zhou, Zaiyan Pingyuan Power Supply Co State Grid Shandong Elect Power Co Dezhou Peoples R China Dezhou Power Supply Co State Grid Shandong Elect Power Co Dezhou Peoples R China

ISBN: (纸本)9798350365573;9798350365580

With the current "dual-carbon" environment, the transform of transportation electrification has become an important way in the pursuit of the "dual-carbon" target, but the uncertainty of the arrival time and charging demand of electric vehicles (EV) has brought great challenges to the design of charging schemes for charging stations (CSs). To maximize the EV users' charging demand, the paper proposes a real-time online energy management strategy for CSs based on Deep-Reinforcement-learning (DRL). First, the control process for EV charging is a Markov decision process, and the Multi-Attention-Actor-Critic (MAAC) algorithm with concentrated training based on a decentralized execution framework is established using the CS as an intelligent. then, an energy management strategy is proposed to maximize the profit of the CS. Finally, the real-time online energy management results of CSs based on DRL are analyzed. the results show that the strategy proposed in this paper can effectively improve the profit of CSs under the assumption of meeting the charging needs of EV users.

关键词： charging control Deep-Reinforcement-learning electric vehicles energy management Markov decision process

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Learnscan: An Interactive learning Platform for Enhanced Education Using Augmented Reality 4

Learnscan: An Interactive Learning Platform for Enhanced Edu...

引用

4th International conference on Ubiquitous Computing and intelligent Information Systems, ICUIS 2024

作者： Kamath, Ashwini Deepikaa, T.R. Godlin Donya, S.P. Pavithra, R. Madhumathi, R. Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331529635

An Interactive learning Platform for Enhanced Education using Augumented Reality (AR) presents the development of an innovative educational website and app designed to enhance learning experiences through the integration of QR codes and Augmented Reality technology. the platform features a curated list of QR codes, each assigned to specific educational topics. Upon scanning a QR code, users are directed to a dedicated website showcasing a detailed 3D model related to the QR code's assigned subject matter. Scanning a QR code associated with 'heart' will lead to a 3D model of the human heart. the website offers an interactive interface that allows users to explore and manipulate the 3D models in real-time. Also, the platform incorporates AR functionality, enabling users to view and interact with the models in their physical environment. It is also aided with audio recordings and description related to the information which is provided in the website. this immersive approach aims to provide a more engaging and effective learning experience, especially for complex subjects where visualizations are beneficial. Key features of the platform include user-friendly navigation, highquality 3D models, and seamless integration with AR technology. © 2024 IEEE.

关键词： 3D modeling

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Research on Unmanned Positioning Technology Based on Factor Graph optimization 4

Research on Unmanned Positioning Technology Based on Factor ...

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4th International conference on intelligent Computing and Human-Computer Interaction, ICHCI 2023

作者： Sima, Haodong Lu, Jingwen Guo, Chence CSSC Haiwei Tech Co. LTD. Zhengzhou450000 China

ISBN: (纸本)9798350343113

A fusion positioning algorithm based on factor graph optimization is proposed to tackle the issues of low positioning accuracy and poor environmental adaptability associated with a single sensor in the unmanned positioning system. this algorithm integrates data from multiple sources, including the Global Positioning System (GPS), Inertial Measurement Unit (IMU), Light Detection and Ranging (LiDAR), and wheel tachometer. through front-end processing and back-end optimization, the algorithm significantly enhances the accuracy and adaptability of the unmanned positioning system. Validated under a scenario of the missing differential signal and laser point cloud features, the fusion positioning algorithm, harnessing factor graph optimization, exhibits substantial accuracy improvements of 84% and 63% over laser-based Simultaneous Localization and Mapping (SLAM) and GPS, respectively. © 2023 IEEE.

关键词： Global positioning system

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A Comparative Study on Enhancing Prediction in Social Network Advertisement through Data Augmentation 4

A Comparative Study on Enhancing Prediction in Social Networ...

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4th International conference on Machine learning and intelligent Systems Engineering (MLISE)

作者： Yang, Qikai Li, Panfeng Xu, Xinhe Ding, Zhicheng Zhou, Wenjing Nian, Yi Univ Illinois Dept Comp Sci Champaign IL 61820 USA Univ Michigan Elect Engn & Comp Sci Ann Arbor MI 48109 USA Columbia Univ Fu Fdn Sch Engn & Appl Sci New York NY USA Univ Michigan Dept Stat Ann Arbor MI 48109 USA Univ Chicago Dept Comp Sci Chicago IL 60637 USA

ISBN: (纸本)9798350375084;9798350375077

In the ever-evolving landscape of social network advertising, the volume and accuracy of data play a critical role in the performance of predictive models. However, the development of robust predictive algorithms is often hampered by the limited size and potential bias present in real-world datasets. this study presents and explores a generative augmentation framework of social network advertising data. Our framework explores three generative models for data augmentation - Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Gaussian Mixture Models (GMMs) - to enrich data availability and diversity in the context of social network advertising analytics effectiveness. By performing synthetic extensions of the feature space, we find that through data augmentation, the performance of various classifiers has been quantitatively improved. Furthermore, we compare the relative performance gains brought by each data augmentation technique, providing insights for practitioners to select appropriate techniques to enhance model performance. this paper contributes to the literature by showing that synthetic data augmentation alleviates the limitations imposed by small or imbalanced datasets in the field of social network advertising. At the same time, this article also provides a comparative perspective on the practicality of different data augmentation methods, thereby guiding practitioners to choose appropriate techniques to enhance model performance.

关键词： Social Network Data Augmentation Deep learning VAE GAN Gaussian Mixture Model

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Reinforced integrated deep transfer learning network based on multi-fault domain transformer fault voiceprint recognition 4

Reinforced integrated deep transfer learning network based o...

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4th International conference on intelligent Power and Systems, ICIPS 2024

作者： Mu, Mingliang Niu, Jianfei Guo, Guangqian Tian, Maoxiang Li, Jinlin Li, Jingyi State Grid Shandong Electric Power Company Binzhou Power Supply Company Binzhou China

ISBN: (纸本)9798331532765

Power transformers emit continuous vibration signals during operation. the signals contain a large number of pulses and fluctuations caused by mechanical faults. they are the main data source for evaluating the operating status of power transformers. In order to study the voiceprint characteristics of vibration acoustic signals of transformers under different operating conditions, a reinforced integrated deep transfer learning network (REDTLN) for multi-fault domain voiceprint recognition is proposed. First, use various new kernel maximum mean difference (kMMD) to build multiple deep transfer learning networks (DTLN) for individual origin single destination field adjustment, so that Make DTLN transferable features diversified. Additionally, a novel unified unsupervised ensemble learning is formulated according to maximum mean difference (MMD) and various metrics, and using this unified measure as motivation, A novel training approach is constructed to search for Efficacious combinations of these DTLNs Rules to achieve enhanced integration of multi-source deep transfer learning networks. Improved accuracy and robustness in dynamic noise environments and complex operating conditions. Empirical outcomes indicate that the suggested model can accurately determine the abnormal operating status of the transformer and diagnose six typical faults of the transformer in different noise environments, with a diagnosis accuracy of more than 99%. © 2024 IEEE.

关键词： Deep reinforcement learning

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Comparative Analysis of Machine learning Algorithms for Predicting Student Academic Performance in Higher Education 4

Comparative Analysis of Machine Learning Algorithms for Pred...

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4th International conference on Ubiquitous Computing and intelligent Information Systems, ICUIS 2024

作者： Cruz, Michael Marvin P. Lumauag, Ramil G. College of Computer Studies Central Philippines State University San Carlos Campus Negros Occidental San Carlos City Philippines Iloilo Science and Technology University Computer Department La Paz Iloilo City Philippines

ISBN: (纸本)9798331529635

In recent years, educational institutions have increasingly sought to leverage data-driven approaches to enhance student success and retention. While numerous machine learning algorithms have been created to forecast academic performance, their efficacy differs among datasets, domains, and institutional contexts. the optimal algorithm for particular settings in higher education remains uncertain. this study aims to compare multiple algorithms such as Random Forest, Support Vector Machine, Decision Tree, Logistic Regression, Naive Bayes, and K-Nearest Neighbors to identify the most effective model for predicting student academic performance in higher education institutions (HEIs). this paper also introduces the application of SMOTE, which focuses on addressing class imbalance. Evaluation of multiple models comparing their performance across metrics such as accuracy, precision, recall, and F1-Score was present in this paper. the results of the comparative analysis showed that Random Forest outperforms other algorithms, particularly when SMOTE is applied, with an accuracy of 97%, demonstrating its robustness and adaptability in handling imbalanced datasets. this paper underscores the capacity of machine learning to furnish educational stakeholders with novel instruments for predicting student outcomes, facilitating prompt interventions to enhance academic performance. © 2024 IEEE.

关键词： Contrastive learning

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Integrating Big Data, ML, and DL for Predictive Analytics in Healthcare: A Comprehensive Analysis and Hybrid Framework Proposal 4

Integrating Big Data, ML, and DL for Predictive Analytics in...

引用

4th International conference on Ubiquitous Computing and intelligent Information Systems, ICUIS 2024

作者： Kargotra, Poonam Parray, Irfan Ramzan School of Computer Science and Engineering Lovely Professional University Phagwara India

ISBN: (纸本)9798331529635

the paper presents a comprehensive in-depth analysis of big data, machine learning (ML), and deep learning (DL) methodologies in predictive healthcare analytics, with a focus on their comparative strengths, research gaps, and applications in real world. the study synthesizes findings from recent research papers related to same objective, highlighting the evolution of data-driven technologies in healthcare sector and the challenges which are associated with data integration, privacy, and real-time processing. the big data analytics provides foundational insights from vast healthcare datasets, while ML algorithms enhance predictive accuracy on structured data, while DL models achieve high precision even in complex tasks, such as medical imaging and real-time IoT embedded systems monitoring. this article introduces a novel hybrid approach that combines the strength of all these approaches, employing common data for heterogenous data integration, federated learning for privacy, and a feedback-driven adaptive system for continuous improvement. Analyzing quantitative comparisons, the effectiveness of each approach is illustrated: DL demonstrate high acuracy, and ML, in turn, flexibility with a moderate rate. Discussing the research gaps of current methodologies, the proposed framework discus the scalability, adaptability, and privacy enhancements of this new conceptual model to create more precise and proactive healthcare analytics. Possible future investigations indicate that it is necessary to enhance these adaptive learning solutions and to expand the arbitrary federative learning approaches to treat different interdisciplinary domains. © 2024 IEEE.

关键词： Federated learning

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Research on Multi-modal Data Management System for intelligent Connected Vehicle 4

Research on Multi-modal Data Management System for Intellige...

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4th IEEE International conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2024

作者： Zhang, Lu Guangxi Vocational Normal University School of Computer and Information Engineering Guangxi China

ISBN: (纸本)9798350363586

the development of intelligent networked vehicle technology and the testing of related algorithms require a large number of datasets as the foundation. the existing datasets are mainly collected from foreign traffic scenarios, which are significantly different from the actual road traffic environment of China. therefore, it is particularly important to construct local datasets and data management systems. this paper describes in detail the design of multi-modal data management system for intelligent Connected Vehicle, covering the overall architecture of systems, hardware composition, the design of software function modules, database design, and key steps for data processing. Experiments and tests verified the reliability and effectiveness of the data management system. © 2024 IEEE.

关键词： Data assimilation

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Artificial Intelligence Analysis for PMSM Servo System Based on Dynamic Parameters 4

Artificial Intelligence Analysis for PMSM Servo System Based...

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4th International conference on Computer Science, Electronic Information Engineering and intelligent Control Technology, CEI 2024

作者： Wang, Shixing Tan, Kangbo Xu, Zhijiang Lu, Yingmei Chen, Long Zhao, Ziwen Huang, Xing Wang, Hao XiDian University School of Electric Engineering Xi'an China Co. Ltd Ningbo China China Electronics Technology Group Corporation 10th Research Institute Chengdu China

ISBN: (纸本)9798350378542

To improve the analyzing efficiency for PMSM servo system considering the dynamic parameters of the insulated-gate bipolar transistor, in this work, a novel approach of an artificial intelligence computational framework for system-level simulation project is proposed. An instance is investigated subsequently, meanwhile, the accuracy of the approach with five machine learning methods is compared. the time consumption and error in test sets prove the high efficiency of the approach to analyze the speed of permanent magnet synchronous motor. this work is helpful for the massive numerical analysis in the design process of electric vehicles. © 2024 IEEE.

关键词： Insulated gate bipolar transistors (IGBT)

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