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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

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the Sophisticated Prognostication of Migraine Aura Using machine learning 4

The Sophisticated Prognostication of Migraine Aura Using Mac...

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4th international conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Samiullah Rehman, Abdul Rehman, Attique Ur Javaid, Sabeen Ali, Tahir Mohammad Mir, Azka Nirsanametla, Yadaiah University of Sialkot Department of Software Engineering Sialkot Pakistan Gulf University for Science and Technology Department of Computer Science Kuwait Lovely Professional University Division of Research and Development Punjab Phagwara144411 India KKR and KSR Institute of Technology and Sciences Andhra Pradesh Guntur India

ISBN: (纸本)9798350353266

Migraine is one of the most disabling diseases in the world and impacting more than one billion individuals. the symptoms such as intensity, Nausea, Vomit, Phonophobia, Photophobia, Visual, Dysphasia, Dysarthria, Vertigo, Sensory, intense to sound, occurs before the migraine. Migraine drains the quality of persons' life. Furthermore, the study of this research was to train machine learning model on migraine aura dataset using different modern approaches that could assist medical patients before occurring migraine and could also give the indications related to migraine. Likewise, by applying random forest algorithm, we got 99.5 percent accuracy using google colab which will be enough to deploy on application for future Project. Moreover, we leveraged traditional framework to complete the research incorporating, Data collection (Online free Kaggle), Preprocessing, Classification, Model training and Comparison of models. As a result, our peak algorithm precision was including, Naive Bayes 94.52%, decision tree 98.3%, K-nearest neighbours 98.6%, random forest 99.5%, Similarly, Rapid miner and Google Colab software are used for the comparison of algorithms and best one is chosen. © 2024 IEEE.

关键词： Random forests

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Research on dynamic network security detection technology based on SDN 4

Research on dynamic network security detection technology ba...

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4th international conference on Network Communication and Information Security, ICNCIS 2024

作者： Zhao, Kai Beijing Polytechnic Beijing100176 China

ISBN: (数字)9781510688261

ISBN: (纸本)9781510688254

Traditional network architecture can no longer meet the development needs of technologies such as cloud computing and big data. SDN network architecture has the characteristics of high openness and programmability, which can quickly respond to changes in business requirements. SDN decouples the control management function of the network from the data forwarding function, simplifies network configuration work based on logic centralized controllers and open programming interfaces, thereby achieving a flat management mode and flexible data forwarding function. the centralized control and programmability of SDN networks also pose significant security threats to SDN. Based on the CSE-CIC-IDS2018 dataset, this article tests the performance of various intelligent learning algorithms, analyzes data flows, distinguishes normal data from abnormal data, improves detection accuracy, and has practical significance for the security defense of SDN networks. © 2025 SPIE.

关键词： Network security computer security Network architectures Control systems machine learning Data transmission Defense and security Detection and tracking algorithms Information security Security technologies

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From random-walks to graph-sprints: a low-latency node embedding framework on continuous-time dynamic graphs 23

From random-walks to graph-sprints: a low-latency node embed...

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4th ACM international conference on AI in Finance (ICAIF)

作者： Eddin, Ahmad Naser Bono, Jacopo Aparicio, David Ferreira, Hugo Ascensao, Joao Ribeiro, Pedro Bizarro, Pedro Feedzai Coimbra Portugal Univ Porto Dept Ciencia Comp Fac Ciencias Porto Portugal

ISBN: (纸本)9798400702402

Many real-world datasets have an underlying dynamic graph structure, where entities and their interactions evolve over time. machine learning models should consider these dynamics in order to harness their full potential in downstream tasks. Previous approaches for graph representation learning have focused on either sampling khop neighborhoods, akin to breadth-first search, or random walks, akin to depth-first search. However, these methods are computationally expensive and unsuitable for real-time, low-latency inference on dynamic graphs. To overcome these limitations, we propose graph-sprints a general purpose feature extraction framework for continuous-time-dynamic-graphs (CTDGs) that has low latency and is competitive with state-of-the-art, higher latency models. To achieve this, a streaming, low latency approximation to the random-walk based features is proposed. In our framework, time-aware node embeddings summarizing multi-hop information are computed using only single-hop operations on the incoming edges. We evaluate our proposed approach on three open-source datasets and two in-house datasets, and compare with three state-of-the-art algorithms (TGN-attn, TGN-ID, Jodie). We demonstrate that our graph-sprints features, combined with a machine learning classifier, achieve competitive performance (outperforming all baselines for the node classification tasks in five datasets). Simultaneously, graphsprints significantly reduce inference latencies, achieving close to an order of magnitude speed-up in our experimental setting.

关键词： Continuous-time dynamic graphs (CTDGs) Streaming graphs Graph representation learning Graph feature engineering Graph Neural Networks

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2023 15th international conference on computer and Automation Engineering, ICCAE 2023

2023 15th International Conference on Computer and Automatio...

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15th international conference on computer and Automation Engineering, ICCAE 2023

ISBN: (纸本)9798350396225

the proceedings contain 109 papers. the topics discussed include: proximity-based reward system and reinforcement learning for path planning;integration of machine learning in agile supply chain management;machine learning model for sentiment analysis on mental health issues;machine learning based power analysis for Simon with secure mechanism;a survey on mapping of urban green spaces within remote sensing data using machine learning & deep learning techniques;a review of deep learning methods for automated clinical coding;student management model integrating e-commerce based on blockchain technology;model for verifying the reliability of candidate data based on blockchain technology;employing thresholding and template rotation to enhance the effectiveness of template matching programs;route prediction from GPS trajectory and road data;augmented reality campus exploration application incorporating equity, diversity, and inclusion;automated detection of biases within the healthcare system using clustering and logistic regression;and artificial intelligence-based smart tele-assisting technology for first-year engineering students.

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Abstractive Text Summarization Using Bert and Adversarial learning 4

Abstractive Text Summarization Using Bert and Adversarial Le...

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4th international conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Prakash, Abhijith Rohith, Gannamaneni Umamageswaran, J. Amrita Vishwa Vidyapeetham Department of Cse Amrita School of Computing Chennai India

ISBN: (纸本)9798350387490

In recent years, the rise in textual data has necessitated the demand and development of efficient text summarization techniques. In order to provide text summaries that are accurate, useful, coherent, informative, and factually accurate, this work introduces a novel framework that combines adversarial learning with BERT's (Bidirectional Encoder Repre-sentations from Transformers) advanced language understanding capabilities. Our methodology is based on the application of a discriminator designed to differentiate between summaries generated by humans and machines. this discriminator directs the generator to produce summaries that mimic the writing styles of humans, improving the summaries' quality and naturalness. We propose a new hybrid loss function that combines adversarial loss with conventional evaluation measures, including ROUGE (Recall-Oriented Understudy for Gisting Evaluation), in addition to the adversarial framework. this dual approach ensures that the model maintains high grammatical accuracy and coherence while simultaneously producing semantically rich summaries. this research has important implications for many applications in Natural Language Processing, especially those that need to extract information from large text data efficiently. © 2024 IEEE.

关键词： Adversarial machine learning

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Indian Premier League Cricket Match Winner Predictor 2025 : A Sports Analytics Study 4

Indian Premier League Cricket Match Winner Predictor 2025 : ...

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4th international conference on Electrical, computer, Communications and Mechatronics Engineering, ICECCME 2024

作者： Arur, Vrushank Mahesh Shanbhag, Aakash Dhananjay Fr. Conceicao Rodrigues College of Engineering 4 Year BE Electronics and Computer Science Bandra Mumbai India Fr. Conceicao Rodrigues College of Engineering BE Electronics and Computer Science Los Angeles United States

ISBN: (纸本)9798350391183

In this paper, we present a comprehensive study on predicting the outcome of Indian Premier League (IPL) cricket matches using machine learning techniques. We utilized a dataset comprising match details and team information, processed to ensure consistency and integrity. Various classification algorithms, including Gradient Boosting, Decision Tree Classifier, Random Forest, and Light Gradient Boosting machine, were employed and optimized using hyperparameter tuning to enhance predictive accuracy. Our findings indicate that the Light Gradient Boosting machine model achieved the highest performance, demonstrating the effectiveness of machine learning in sports analytics. the study also highlights the importance of features such as player performance, team strength, and match conditions in predicting match outcomes. these insights not only advance the application of predictive modeling in sports but also provide valuable tools for analysts and enthusiasts to understand and anticipate game results. © 2024 IEEE.

关键词： Gradient Boosting Hyperparameter Tuning LGBM Match Prediction ML

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Online Q&A for machine learning Courses Based on Formula Recognition 4

Online Q&A for Machine Learning Courses Based on Formula Rec...

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4th international conference on Information Science and Education, ICISE-IE 2023

作者： Zhu, Xiaoke Chen, Xiaopan Li, Minghao Zhang, Tianxiang School of Computer and Information Engineering Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng China Henan University Kaifeng Henan Key Laboratory of Big Data Analysis and Processing China

ISBN: (纸本)9798350394610

Mathematical formulas are ubiquitous in courses such as machine learning. Understanding these formulas is the primary challenge for studying of students. If a searchable knowledge base can be built for these formulas, it will help students learn. Accurate identification of these formulas is an important step in building and querying the knowledge base. However, mathematical formulas have complex two-dimensional and nested structures, which makes them inconvenient to search like ordinary text. this paper employs Faster R-CNN to detect regions containing mathematical formulas and improves its backbone network, anchor box dimensions, and pre-training network to make it more accurate and efficient in identifing these formulas. Furthermore, in order to enhance the features and meaningful representation of mathematical formulas in the input image, we introduce the distance transformation. Finally, comparative experiments were conducted on the publicly dataset GTDB. Our proposed approach achieved recognition rates of 91.22% and 85.74% for isolated and hybrid formulas detection. © 2023 IEEE.

关键词： Deep learning

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Research and application of Fault Diagnosis and Localization Techniques for Power Communication Optical Cables Based on Multimodal Data Analysis 4

Research and Application of Fault Diagnosis and Localization...

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4th IEEE international conference on Data Science and computer application, ICDSCA 2024

作者： Zhang, Xuguang Zhang, Bo Guo, Bo Ren, Hang Wang, Wanxin Ma, Chao State Grid Jilin Electric Power Co. Ltd. Tonghua Power Supply Company Jilin China

ISBN: (纸本)9798350368239

Aiming at the problems of low accuracy of power cable fault pattern recognition and large error of fault location, a method of power cable fault diagnosis and location based on Beluga Whale Optimization-Extreme learning machine (BWO-ELM) algorithm and GIS system is proposed. First, the beluga whale optimization (BWO) algorithm is used to optimize the initial parameters of the extreme learning machine (ELM), and the BWO-ELM multi classification Optical Time Domain Reflectometry (OTDR) curve analysis fault pattern recognition method is constructed, obtain the straight-line distance and type of the optical fiber fault point to lay the foundation for fault location. Secondly, the accurate positioning method of optical cable fault based on GIS is proposed, which converts the straight-line distance of the optical fiber fault point into the optical cable distance, and matches it with the actual geographical location to obtain the coordinates of the actual fault point, which are intuitively displayed in the GIS system. Finally, the application effect of the proposed method is verified by simulation experiments. the results show that the proposed method achieves a fault pattern recognition accuracy of about 98.66%, the fault location error is floating up and down 3M, and the average error is 1.481%. It has higher performance and accuracy than other recognition models and fault location methods. ©2024 IEEE.

关键词： Optical fibers

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Deep learning Point Cloud Classification Algorithm Considering Local Spatial Features 4

Deep Learning Point Cloud Classification Algorithm Consideri...

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4th international conference on computer Engineering and application, ICCEA 2023

作者： Liu, Yue Sun, Ping School of Artificial Intelligence Shenyang University of Technology Shenyang110870 China

ISBN: (纸本)9798350347548

In the process of feature extraction of point cloud data, most of the existing methods ignore the rich spatial geometric information in point cloud data, resulting in poor classification accuracy To solve this problem, this paper proposes a point cloud depth learning classification algorithm considering the geometric characteristics of local space the LANet network model is constructed, and a local spatial feature network is proposed to learn the rich spatial geometric relationships in the point cloud data, and the attention mechanism is added to the network to learn the weight of the feature channel on the classification, so as to further improve the accuracy of point cloud classification the simulation experiment on the ModelNet40 classification dataset shows that the local spatial feature learning network can extract complete spatial geometric information, and the attention mechanism can optimize the classification network. Compared with the current mainstream networks, the proposed method can effectively learn point cloud data and help improve the accuracy of point cloud classification. © 2023 IEEE.

关键词： Classification (of information)

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Intelligent Caching Strategies for 5G Edge Networks using machine learning 4

Intelligent Caching Strategies for 5G Edge Networks using Ma...

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Guduri, Naga Venkata Ramakrishna Sethu, S. Shalinirajan, R. Reena, R. Karthikeyan, M. Uthayakumar, G.S. University of Technology and Applied Sciences-Al Musannah Electrical and Electronics Section Engineering Department Oman Department of Computer Science and Engineering Tamilnadu Chennai India Panimalar Engineering College Department of Computer Science and Business System Tamilnadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India Tamilnadu Chennai India St. Joseph's Institute of Technology Department of Electronics and Communication Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350300888

To reduce latency and improve the user experience, content caching solutions at the network periphery will become increasingly important as 5G networks and data-intensive applications. In this proposed work, the deployment of a caching architecture for 5G periphery networks using machine learning is done. Utilizing information on user behavior, network conditions, and content prominence enables more accurate forecasts and cache management decisions. this trains the prediction models using a wide range of machine-learning techniques to accommodate the ever-changing needs of both networks and content. To evaluate the efficiency of the proposed architecture by measuring its ability to increase cache hit rates and decrease latency. these techniques significantly outperform the standard caching methods. With the intelligent caching architecture of 5G periphery networks, throughput is increased, latency is reduced, and congestion is alleviated. the findings of this study contribute to the growing corpus of literature on AI-driven optimization in 5G networks and shed light on how such solutions could be enhanced for future networks. Neural Networks achieved 96% accuracy than SVM which is 93%. © 2023 IEEE.

关键词： 5G mobile communication systems

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