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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"

共 4179 条记录，以下是621-630 订阅

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Research on the application of a hybrid model based on deep learning and cluster analysis in anomaly detection of cloud platform access 4

Research on the application of a hybrid model based on deep ...

引用

4th international conference on computer Vision and Pattern Analysis, ICCPA 2024

作者： Li, Guohua Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou China Guangxi Colleges and Universities Key Laboratory of Intelligent Industry Software Wuzhou University Wuzhou China

ISBN: (纸本)9781510682528

With the rapid development of digitalization, cloud platforms have become a key technology supporting remote networking. However, the accompanying cybersecurity issues, particularly the detection of anomalous access activities, pose significant challenges in maintaining platform stability and user data security. this paper proposes a hybrid model that integrates a one-dimensional convolutional neural network (1D-CNN) with the DBSCAN clustering algorithm, aimed at enhancing the detection accuracy of anomalous access behaviors in cloud platforms. through in-depth analysis and feature extraction of network traffic data, combined with the effective identification of abnormal patterns by the clustering algorithm, this study not only improves detection efficiency but also enhances the model's adaptability to novel anomalous behaviors. Experimental results demonstrate that this model surpasses traditional detection methods in multiple performance metrics such as accuracy, precision, recall, and F1 score, confirming its feasibility and effectiveness in practical application scenarios. © 2024 SPIE.

关键词： Cloud platforms

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machine learning Classifiers to Decrease Diabetic Patients Probability of Hospital Readmission 4

Machine Learning Classifiers to Decrease Diabetic Patients P...

引用

4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： thenappan, S. Ramshankar, N. Hemavathy, N. Subashree, V. Manjul, Ravi Ranjan Rajeswari, J. Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India R. M. D. Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India Velammal Engineering College Department of Electronics and Communication Engineering Tamilnadu Chennai India Ramchandra Chandarvansi Institute of Technology Department of Mechanical Engineering JharkhandBishrampur Palamu India Agni College of Technology Department of Electronics and Communication Engineering Tamilnadu India

ISBN: (纸本)9798350300093

Diabetes is a chronic condition that affects millions of individuals around the globe. Unfortunately, patients with diabetes have a significant risk of hospital readmission following discharge. Using machine learning classifiers, proposed a strategy to reduce the risk of readmission for 768 diabetes patients. this utilized a dataset containing patient demographic information, medical history, and laboratory results which are collected from Pima India Dataset. To predict the risk of readmission, and train several machine learning models, including Logistic Regression, Random Forest, and Support Vector machines. the research indicates that the Random Forest model has the maximum degree of precision, at 97%. Age, glucose level, Hba1c levels, and comorbidities have been identified as the most significant factors contributing to the risk of readmission. On the basis of these findings, this recommends a personalized approach to diabetes management that takes individual patient characteristics and risk factors into account. this strategy could reduce the likelihood of readmission for diabetic patients and enhance their overall health. © 2023 IEEE.

关键词： Hospitals

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Federated learning: Concepts, application and Future Scope 4th

Federated Learning: Concepts, Application and Future Scope

引用

4th international conference on Smart IoT Systems: Innovations and Computing, SSIC 2023

作者： Jain, Pallav Patidar, Sanjay Delhi Technological University New Delhi India

ISBN: (纸本)9789819751990

Artificial is a constantly evolving and progressing technology. In today’s society, artificial intelligence models are frequently employed. With today’s data and the capabilities of artificial intelligence, we can attain human intellect and, in some situations, considerably more. But also, the rapid increase of data and how it is managed is a cause for concern. machine learning is an artificial intelligence sub-area that allows a machine to learn from prior data. Nowadays, machine learning is utilized in a broad range of applications. However, it is a tough task to apply machine learning on decentralized data. In the recent years, a new technique known as Federated learning has garnered considerable attention. It concentrates to train distributed and decentralized data while maintaining its privacy. In our paper we have explained what Federated learning is, the characteristics of Federated learning, what are the hardships we face in applying Federated learning, in which field Federated learning is currently used and what are the future scope of Federated learning. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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A Generative Approach for Comprehensive Financial Event Extraction at the Document Level 23

A Generative Approach for Comprehensive Financial Event Extr...

引用

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

作者： Zou, Jinan Liu, Yanxi Qi, Yuankai Cao, Haiyao Liu, Lingqiao Shi, Javen Qinfeng Univ Adelaide Australian Inst Machine Learning Adelaide SA Australia Univ Adelaide Adelaide SA Australia

ISBN: (纸本)9798400702402

Financial event extraction enables the extraction of comprehensive and accurate information about financial events from documents. this paper explores the current methods for extracting events at the financial document level, which often involve custom-designed networks and processes. We question whether such extensive efforts are truly necessary for this task. Our research is motivated by recent developments in generative event extraction, which have shown success in sentence-level extraction but have yet to be explored for financial document-level extraction. To fill this gap, we propose a generative solution for document-level event extraction, which is more challenging due to the presence of scattered arguments and multiple events. We introduce an encoding scheme to capture entity-to-document level information and a decoding scheme that makes the generative process aware of all relevant contexts. Our results indicate that using our method, a generative-based solution can perform as well as state-of-the-art methods that use a specialized structure for document event extraction, providing an easy-to-use, strong baseline for future research.

关键词： natural language processing financial document event extraction

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application of computer 3D Visualization Image Technology in Urban Architecture Planning System 4

Application of Computer 3D Visualization Image Technology in...

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4th international conference on computers, Information Processing and Advanced Education, CIPAE 2023

作者： Wei, Cui Guangling College of Yangzhou University Yangzhou China

ISBN: (纸本)9798350342710

A new visualization technique for urban space is proposed. the topography and geomorphology of DGN model were studied by using MicroStation, ArcGIS and VC++. then it introduces how to use ArcGIS to generate TIN. the original error is removed through man-machine dialogue, and the DEM result is obtained. this paper presents a complete hierarchical quadtree and aggregation data organization model. Experimental results show that this algorithm has good classification ability, and its classification accuracy can reach 98.13%. the rate of false detection and missed diagnosis were 0 and 1.87%, respectively. the proposed method has good performance. © 2023 IEEE.

关键词： Smart city

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Revolutionizing IoT Network Security with Deep learning-Anomaly Detection Model 4

Revolutionizing IoT Network Security with Deep Learning-Anom...

引用

4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Vyas, Manish Vijayaganth, R. Chandhok, Jigyasha Srivastava, Aviral Arumugam, S. Tiwari, Mohit College Name & Address: Vikrant Institute of Technology and Management Computer Science Department Indore India Artificial Intelligence and Data Science M.Kumarasamy College of Engineering Thalavapalayam Karur639113 India Tula's Institute of Engineering and Management Naac Grade A+ Accredited Institute Computer Science & Engineering UttarakhandDhoolkot Dehradun India Amity University Rajasthan NH 11 C kant kalwar Jaipur300202 India Periyar Maniammai Institute of Science & Technology Periyar Nagar Vallam Computer Science and Applications Thanjavur613403 India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering A-4 Rohtak RoadPaschim Vihar Delhi India

ISBN: (纸本)9798350300093

the term 'Internet of things' (IoT) is used to describe the collection of data and the connectivity of items to the web that requires little to no human interaction. the IoT is a network of interconnected devices that can collect and disseminate information. Increased security and privacy worries accompany the launch of new devices due to the proliferation of Internet connections and the development of cutting-edge technology like the IoT. these days, the IoT is used everywhere, but especially in logistics, manufacturing, and healthcare. While these emerging IoT applications greatly enhance the usefulness of smart objects, they also present new security risks. Because of this, adapting existing intrusion detection systems (IDS) for use with IoT networks is a topic of intense study. Many IDS experts have found success with machine learning (ML) and deep learning (DL) techniques. By combining deep extraction through the Convolutional autoencoder with deep learning to identify the best features, this work delivers an improved IDS that can be used for anomaly detection. Improves to the deep learning approach include an evaluation of hyperparameter effectiveness, a stage of feature pruning using an autoencoder neural network, and an examination of the sturdiness of the most effective deep neural networks for circumstances exaggerated by Gaussian noise over some of the features in question. Despite the noise, the results show that the formed IoT dataset is useful for anomaly detection with deep learning methods. © 2023 IEEE.

关键词： Internet of things

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Sentiment Analysis of Social Media Reviews using machine learning and Word Embedding Techniques 4

Sentiment Analysis of Social Media Reviews using Machine Lea...

引用

4th IEEE Annual Flagship India Council international Subsections conference, INDISCON 2023

作者： Lavanya, B.N. Shenoy, P Deepa Venugopal, K.R. Visvesvaraya College of Engineering Bangalore University Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350333558

the enormous amount of data generated and exchanged daily on social media raises the need for knowledge-extraction tasks to be mechanized. Sentiment analysis is an active area of information extraction research with various challenges to overcome. Any business ought to be highly interested in client feedback. Customers mainly rely on reviews to determine where to dine. Sentiment analysis is vital in classifying restaurant reviews into positive, negative, and neutral polarity to determine whether the cuisine is good, securely produced, and worth picking over other restaurants. the Yelp dataset for restaurant reviews is used in this study to test different word embedding approaches, including Bag of Words (BoW), Term Frequency-Inverse Document Frequency(TF-IDF), GloVe, Word2Vec, and Doc2Vec. Supervised machine learning (ML) algorithms like Logistic Regression (LR) and Support Vector machine (SVM) are evaluated based on the performance metrics such as the F1-Score, Accuracy, Recall, and Precision. Comparative findings demonstrate that combining a Support Vector machine and TF-IDF word embedding technique produced results with an accuracy of 98%. © 2023 IEEE.

关键词： Sentiment analysis

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A Deep learning for Cardiovascular Diseases Detection on Wearable Devices Data 4

A Deep Learning for Cardiovascular Diseases Detection on Wea...

引用

4th IEEE international conference on Smart Information Systems and Technologies, SIST 2024

作者： Baikuvekov, Meirzhan Tursynova, Azhar Yespayev, Galymzhan Department of Information Systems Al-Farabi Kazakh National University Almaty Kazakhstan Department of AI and Big Data Al-Farabi Kazakh National University Almaty Kazakhstan Satbayev University Almaty Kazakhstan

ISBN: (纸本)9798350374865

this research paper explores the potential of deep learning techniques in the early detection of cardiovascular diseases using data collected from wearable devices. With the advent of advanced wearable technology, continuous monitoring of physiological parameters, such as heart rate, electrocardiogram (ECG) signals, and physical activity patterns, has become feasible. Leveraging the power of deep neural networks, we investigate the development of predictive models capable of identifying individuals at risk of cardiovascular diseases. the study employs rigorous data preprocessing techniques, encompassing feature extraction and data segmentation, in conjunction with machine learning algorithms to enhance model accuracy. Additionally, regularization methods, notably dropout, are harnessed to address challenges posed by diverse datasets. the research envisions the incorporation of data derived from everyday activities to comprehensively assess their impact on heart health, ultimately aiming to proactively alert individuals to deteriorating cardiovascular conditions. the findings presented herein encourage further exploration and innovation in the realm of cardiovascular disease detection, fostering advancements that can enhance public health and well-being. © 2024 IEEE.

关键词： Heart

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Pneumonia Detection Using Hybrid CNN Model 4

Pneumonia Detection Using Hybrid CNN Model

引用

4th IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Chaitanya Krishna, C. Rajendran, P. Naga Chaitanya, C.A. Mohamed Iabal, M. School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India CMR Institute of Technology Computer Science and Engineering Hyderabad501401 India

ISBN: (纸本)9798350306118

this medical research introduces a hybrid model using transfer learning from DenseNet201, InceptionResNetV2, and ResNet50 for superior pneumonia detection. Emphasizing the crucial value of early identification, we combine features from these architectures to enhance accuracy. Rigorous experiments on a sizable chest X-ray dataset showcase our model's superiority, achieving 95.67% accuracy, surpassing individual architectures and conventional methods. the model's improved generalization to new cases is attributed to the synergy of transfer learning and diverse architectures. Furthermore, we explore the interpretability of predictions for potential clinical insights. By leveraging these advanced techniques, our project contributes to more effective early-stage pneumonia detection, promising better health care outcomes. © 2023 IEEE.

关键词： Deep learning

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IO Behaviour Analysis Based on machine learning 4

IO Behaviour Analysis Based on Machine Learning

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4th international conference on machine learning and computer application, ICMLCA 2023

作者： Pan, Weibo Li, Fei Li, Sheng Zhao, Yongcai Zhang, Jun Yang, Di Wan, Xiang The 9th Research Laboratory Wuhan Second Ship Design and Research Institute Wuhan430205 China Quality Management Department Wuhan Second Ship Design and Research Institute Wuhan430205 China The 5th Research Laboratory Wuhan Second Ship Design and Research Institute Wuhan430205 China The 1st Research Laboratory Wuhan Second Ship Design and Research Institute Wuhan430205 China

ISBN: (纸本)9798400709449

this article uses machine learning algorithms to predict the latency of an IO request without additional internal information on the device, treating it as a black box to model equipment performance prediction. Different from algorithms such as CART in machines used in amounts of IO performance prediction, this paper mainly analyzes the impact of attributes on prediction performance and implements and compares the performance of multiple algorithms on different workloads. Based on the GBDT algorithm and the attribute selection algorithm, it analyzes different metrics (attribute sets). It proposes that using a "lastdelay"attribute can improve the prediction effect and takes removing the distance attribute from SSDs based on traditional metrics into consideration. According to unstable effects on SSDs and unstable performance of the distance attribute, this paper points out the necessity of using different metrics for different loads and different devices. © 2023 ACM.

关键词： Forecasting

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