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共 2812 条记录，以下是131-140 订阅

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Preserving Accuracy in Federated learning via Equitable Model and Efficient Aggregation 6th

Preserving Accuracy in Federated Learning via Equitable Mo...

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6th international conference on Recent Trends in Image Processing and pattern recognition, RTIP2R 2023

作者： Mehdi, Muntazir Makkar, Aaisha Conway, Myra Sama, Lakshit University of Derby DerbyDE22 1GB United Kingdom Capgemini Engineering Adelaide Australia

ISBN: (纸本)9783031530814

machine learning has revolutionized research by extracting complicated patterns from complex data, particularly in healthcare and medical imaging, where accurate diagnosis is critical. the concept of federated learning has gained popularity in the field of machine learning as a viable technique for addressing privacy issues in distributed settings. this research explores federated learning in healthcare, demonstrating its capability to achieve results comparable to centralized data while enhancing the accuracy of deep learning models for clinical data interpretation. To ensure reliable model performance during federated learning rounds, this study introduces a proactive mechanism for coordinating server updates with equitable client modifications. the equitable model, designed to reduce accuracy fluctuations, consistently improves accuracy across multiple training rounds on a non-IID dataset. We achieved smooth accuracy improvement by implementing the novel Equitable model, resulting in robust model development. As healthcare AI continues to advance, federated learning emerges as a critical tool for developing precise prediction models while preserving patient data privacy and aligning with increasingly strict data standards worldwide, such as GDPR regulations. this strategic approach not only promotes ethical, efficient, and secure progress in medical research and practice, but it also emphasizes the importance of protecting patient data privacy while utilizing machine learning’s potential. © 2024, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Clinical research

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Vietnamese Text Summarization Using Transformer Approach 6th

Vietnamese Text Summarization Using Transformer Approach

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6th international conference on Computational Intelligence in pattern recognition, CIPR 2024

作者： Hung, Bui thanh Tan, Phan Nguyen Trong Long, Tran Ho Phi Data Science Laboratory Faculty of Information Technology Industrial University of Ho Chi Minh city Ho Chi Minh Viet Nam

ISBN: (纸本)9789819780891

Text summarization is a crucial aspect of natural language processing, finding widespread applications in various aspects of our lives. Numerous studies have delved into this task, employing diverse approaches ranging from linguistic analysis to machine learning and deep learning methodologies. However, there has been limited exploration of transformer-based models for Vietnamese text summarization. In our research, we leverage transformer models, namely, PhoBERT, BARTpho, and ViT5, to develop and refine effective summarization models. through experimentation on the Vietnews dataset and evaluation using ROUGE metrics, our proposed method demonstrates noteworthy achievements in the realm of Vietnamese text summarization, showcasing its potential for advancing the quality of summarization tasks. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： BARTpho BERT Deep learning PhoBERT Text summarization Transformer ViT5

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Unmasking Disinformation: Detection of Fake News Online Using learning Techniques 6

Unmasking Disinformation: Detection of Fake News Online Usin...

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6th IEEE international conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Shaikh, Numair Petkar, Pallavi Sonawane, Sheetal SCTR's Pune Institute of Computer Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350306613

In an era where fake news proliferates effortlessly across social media platforms, the task of discerning reliable news sources has become increasingly challenging. Detecting fake news in the realm of social media presents unique characteristics and complexities that render traditional detection algorithms ineffective. Recent research endeavors have sought to address these challenges by leveraging a diverse array of methods, including machine learning, deep learning, feature engineering and graph mining. In our research, we investigate the detection of fake news by employing a combination of traditional machine learning algorithms and deep learning models for fake news classification. Furthermore, we extend our research by conducting real-Time analysis for fake news detection using Twitter data and data from fact-checking sites. By combining the strengths of traditional machine learning and deep learning, our research offers a holistic approach to fake news detection, enhancing the accuracy and robustness of our results. © 2024 IEEE.

关键词： Fake News Detection Natural Language Processing Text Classification

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AF-KNN: A machine learning-based Algorithm for Automatic recognition of Logistics Numbering Images 6

AF-KNN: A Machine Learning-based Algorithm for Automatic Rec...

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6th IEEE international conference on Automation, Electronics and Electrical Engineering, AUTEEE 2023

作者： Yang, Lanqi Wang, Jianjun Chen, Ao Ma, Wenjia University of Chinese Academy of Sciences Institute of Cyberspace Security Beijing China China United Network Communications Co. Ltd. Heilongjiang Branch Harbin China Donghua University College of Information Science and Technology Shanghai China Tiangong University School of Electronics & Information Engineering Tianjin China

ISBN: (纸本)9798350305623

With the development of IoT intelligence and automation, the demand for efficient and accurate recognition of logistics numbering image processing is increasing. In response to the high computational complexity of IoT environments and the need for higher efficiency, precision, and real-time recognition, this paper proposes an adaptive ensemble algorithm called Adaptive Fusion-KNN, based on KNN. AF-KNN integrates two weak classifiers based on KNN and utilizes machine learning methods to adaptively train these weak classification models. It dynamically adjusts its own weight in the voting process based on the error rate from the previous round of training, thereby ensuring more reliable label prediction results from the strong classifier. the introduction of this algorithm provides a theoretical basis for efficient, accurate, and automated classification of data in IoT application scenarios. © 2023 IEEE.

关键词： automatic recognition of images IoT KNN logistics number machine learning

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Strategic Decisions using machine learning with Interpretative Structural Modelling (ISM) on Digital Platform data for Marketing Intelligence 6

Strategic Decisions using Machine Learning with Interpretati...

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6th international conference on Inventive Computation Technologies, ICICT 2023

作者： Chaudhari, Vipul Damle, Madhavi Pune India

ISBN: (纸本)9798350398496

S ystematized strategic decisions using marketing intelligence gathered through data mining and machine learning using open-source digital platforms are the focal theme of this research. the parameters that affect product & service marketing are considered here with their interlinkages in making effective, sustainable strategic decisions, thus aiding in expounding the model's dimensions for market intelligence and analysis. this article is a conceptual, descriptive research study in which the investigative technique usedin this research is conducted based on expert opinions to derive a model using Interpretive Structural Modeling. the data from digital platforms using machine learning can be converted into marketing intelligence linked to various marketing factors for their interrelations model derived through Interpretative Structural Modelling (ISM) technique into well-defined and structured models for making effective, strategic decisions. this research study describes the dimensions linked with a specific product outcome as an illustration of a mobile phone model for specific linkages strengths for strategic decisions. A few studies focused on leveraging data from digital platforms have different approaches to gathering marketing intelligence for digital platforms, significantly linking it with attributes for strategic decision-making. this study uses ISM for specific product sentiments and marketing factors to derive a model for effective strategic decisions. © 2023 IEEE.

关键词： data mining

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Breast Cancer Prediction Using Classical machine learning Models 6

Breast Cancer Prediction Using Classical Machine Learning Mo...

引用

6th international conference on Intelligent Robotics and Control Engineering, IRCE 2023

作者： Almazrouei, Khawla Nassif, Ali Bou Autonomous Robotics Research Center UAE Technology Innovation Institute Abu Dhabi United Arab Emirates College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9798350326338

machine learning is an emerging topic of study nowadays. Due to their extensive capacity for data exploration and the discovery of illuminating patterns, machine learning, and data mining techniques have become extremely popular. data mining techniques are also used in the healthcare industry and have proven particularly effective for cancer prognosis and prediction. Breast cancer is the second most significant factor in women's cancer-related death rates. In that situation, early detection of benign and malignant stages of breast cancer can enable the patient, and the doctor administers the appropriate treatments. In this study, a breast cancer dataset of 569 instances and 32 features were chosen to predict the stages of breast cancer. Four classification algorithms are employed: Support Vector machine, K Nearest Neighbor, Multi layer Perceptron, and Radial Basis Neural Network. the algorithm's performance regarding Accuracy, Precision, Recall, and Roc Area are compared, and it is found that SVM outperformed the others with an accuracy rate of 98.05%. the paper also demonstrated how feature selection strategies might shorten training times before proposing a reliable and universal model for predicting the breast cancer stage. © 2023 IEEE.

关键词： Multilayer neural networks

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Solar Flare Forecasting with Deep learning-based Time Series Classifiers 26

Solar Flare Forecasting with Deep Learning-based Time Series...

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Ji, Anli Wen, Junzhi Angryk, Rafal Aydin, Berkay Georgia State Univ Dept Comp Sci Atlanta GA 30302 USA

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Over the past two decades, machine learning and deep learning techniques for forecasting solar flares have generated great impact due to their ability to learn from a high dimensional data space. However, lack of high quality data from flaring phenomena becomes a constraining factor for such tasks. One of the methods to tackle this complex problem is utilizing trained classifiers with multivariate time series of magnetic field parameters. In this work, we compare the exceedingly popular multivariate time series classifiers applying deep learning techniques with commonly used machine learning classifiers (i.e., SVM). We intend to explore the role of data augmentation on time series oriented flare prediction techniques, specifically the deep learning-based ones. We utilize four time series data augmentation techniques and couple them with selected multivariate time series classifiers to understand how each of them affects the outcome. In the end, we show that the deep learning algorithms as well as augmentation techniques improve our classifiers performance. the resulting classifiers' performance after augmentation outplayed the traditional flare forecasting techniques.

关键词： solar flare prediction deep neural networks data augmentation

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Medical Image Generation Techniques for data Augmentation: Disc-VAE versus GAN 6

Medical Image Generation Techniques for Data Augmentation: D...

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6th international conference on pattern Analysis and Intelligent Systems, PAIS 2024

作者： Rais, Khadija Amroune, Mohamed Haouam, Mohamed Yassine Tebessa12002 Algeria

ISBN: (纸本)9798350350265

AI algorithms can analyze medical images, such as X-rays, MRIs, and CT scans, assisting doctors in detecting diseases and making more accurate diagnoses. However, large, diverse datasets of medical images are critical for training robust and effective AI models. Furthermore, acquiring and annotating these datasets poses considerable challenges due to factors such as cost, privacy concerns, and data scarcity. To address these challenges, data augmentation techniques, particularly synthetic image generation, have become more popular in recent years. Traditional augmentation methods such as rotation, scaling, and cropping are commonly used but may not capture all the characteristics of medical *** paper explores various techniques for generating synthetic medical images to enhance AI models. We examine recent methodologies of data augmentation employed for generating synthetic medical images. Additionally, we highlight the importance of synthetic images in improving the performance and generalization capabilities of machine learning algorithms in the medical field. this is achieved by adding the discriminator into the VAE, Disc-VAE, and comparing it with GAN through the quantitative metrics (SSIM, PSNR, and Accuracy). © 2024 IEEE.

关键词： Computerized tomography

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Online Adaptive Metrics for Model Evaluation on Non-representative Offline Test data 26

Online Adaptive Metrics for Model Evaluation on Non-represen...

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Piovano, Enrico Le, Dieu-thu Chen, Bei Bradford, Melanie Amazon Com Inc Bellevue WA 98004 USA

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

A major challenge encountered in the offline evaluation of machine learning models before being released to production is the discrepancy between the distributions of the offline test data and of the online data, due to, e.g., biased sampling scheme, data aging issues and occurrence(s) of regime shift. Consequently, the offline evaluation metrics often do not reflect the actual performance of the model online. In this paper, we propose online adaptive metrics, a computationally efficient method which re-weights the offline metrics based on calculating the joint distributions of the model hypotheses over the offline test data VS. the online data. It provides offline metrics which estimate the production performance of the model by taking into account the test data biases. the proposed method is demonstrated by real life examples on text classification and a commercial natural language understanding system. We show that the online adaptive metrics can provide accurate predictions of online recall and precision even with a small test dataset.

关键词： Measurement Adaptation models Computational modeling Text categorization Production machine learning data models

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6th international conference on communication and computational technologies, ICCCT 2024

6th International conference on communication and computatio...

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6th international conference on communication and computational technologies, ICCCT 2024

ISBN: (纸本)9789819774258

the proceedings contain 39 papers. the special focus in this conference is on communication and computational technologies. the topics include: Conceptual Framework for Risk Mitigation and Monitoring in Software Organizations Based on Artificial Immune System;a Multilevel Home Fire Detection and Alert System Using Internet of things (IoT);smart Baby Warmer with Integrated Weight Sensing;a Robust Multi-head Self-attention-Based Framework for Melanoma Detection;Domain Knowledge Based Multi-CNN Approach for Dynamic and Personalized Video Summarization;Efficient Information Retrieval: AWS Textract in Action;text Summarization Techniques for Kannada Language;Parkinson’s Detection From Gait Time Series Classification Using LSTM Tuned by Modified RSA Algorithm;human Action recognition Using Depth Motion Images and Deep learning;maximizing Portfolio Returns in Stock Market Using Deep Reinforcement Techniques;Detecting AI Generated Content: A Study of Methods and Applications;A Systemic Review of machine learning Approaches for Malicious URL Detection;digital Image Forgery Detection Based on Convolutional Neural Networks;Banana Freshness Classification: A Deep learning Approach with VGG16;greenHarvest: data-Driven Crop Yield Prediction and Eco-Friendly Fertilizer Guidance for Sustainable Agriculture;real-Time Deep learning Based Image Compression Techniques: Review;fog-Cloud Enabled Human Falls Prediction System Using a Hybrid Feature Selection Approach;a 4-Input 8-Bit Comparator with Enhanced Binary Subtraction;multivalued Dependency in Neutrosophic database System;traffic Sign recognition Framework Using Zero-Shot learning;machine learning Techniques to Categorize the Sentiment Analysis of Amazon Customer Reviews;alzheimer’s Disease Diagnosis Using machine learning and Deep learning Techniques;sentinel Eyes Violence Detection System;Detection of Alzheimer’s Disease from Brain MRI Images Using Convolutional Neural Network.

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