Application of Machine Learning Algorithm Based on Big Data

Wang, Yufei

doi:10.1007/978-3-031-05237-8_130

Cited by 5 publications

(8 citation statements)

References 8 publications

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“…2. Exploratory Data Analysis (EDA) [28]: Examining the relationships between various features and customer churn to identify significant predictors.…”

Section: Experimental Designmentioning

confidence: 99%

Utilizing Data Science and AI for Customer Churn Prediction in Marketing

Ang Li,

Yang,

Zhan

et al. 2024

JTPES

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This study explores the application of data science and AI techniques in predicting customer churn within the telecommunications industry, a sector characterized by intense competition and high customer turnover rates. By analyzing historical customer data, including usage patterns and service preferences, the study aims to identify factors contributing to churn and propose targeted retention strategies to mitigate losses. Traditional classification algorithms and ensemble techniques are evaluated using the Telecom-Customer-Churn dataset, with emphasis on the underutilized Stacking ensemble method. The results demonstrate that ensemble learning algorithms, particularly the Stacking model, outperform single algorithms, with CatBoost exhibiting the highest accuracy at 0.8119, followed closely by RandomForest at 0.7902 and XGBoost at 0.7820. These findings underscore CatBoost's superior generalization capabilities, likely attributed to its adept handling of categorical features and missing values, and its ability to model complex data relationships. The study contributes to advancing understanding of ensemble models and offers valuable insights for predicting telecom customer churn, thereby aiding in the development of effective retention strategies and enhancing customer satisfaction and loyalty.

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“…2. Exploratory Data Analysis (EDA) [28]: Examining the relationships between various features and customer churn to identify significant predictors.…”

Section: Experimental Designmentioning

confidence: 99%

Utilizing Data Science and AI for Customer Churn Prediction in Marketing

Ang Li,

Yang,

Zhan

et al. 2024

JTPES

View full text Add to dashboard Cite

show abstract

“…In the field of intelligent security, image recognition technology can be used to monitor people and objects in cameras to achieve abnormal behavior detection and security early warning. [17]In the field of medical health, image recognition technology can help doctors diagnose diseases in medical images (such as Xrays, and MRI images) and improve diagnostic accuracy and efficiency. In the field of autonomous driving, image recognition technology is the key to realizing the vehicle's perception of the environment, and can identify traffic participants such as roads, vehicles, and pedestrians, and realize intelligent driving decisions.…”

Section: Image Recognitionmentioning

confidence: 99%

“…The method learns a discriminant network classifier D, which maximizes the mutual information between input x and label y for K unknown classes by making conditional distribution predictions [31]. To help these classifiers generalize better to unknown data, the robustness of the classifier is imposed on 4023 (Online), Journal of Artificial Intelligence General Science (JAIGS) 56 manipulate the repair results, the defective part of the image is generated from scratch, mainly can be applied to image deblurring and image denoising.…”

Section: Catgan Image Processing and Optimization Principlementioning

confidence: 99%

“…The method learns a discriminant network classifier D, which maximizes the mutual information conditional distribution predictions [31]. To help these classifiers generalize better to unknown data, the robustness of the classifier is imposed on samples generated by adversarial generative models that try to trick the classifier into accepting forged input samples.…”

Section: Catgan Image Processing and Optimization Principlementioning

confidence: 99%

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Image Processing and Optimization Using Deep Learning-Based Generative Adversarial Networks (GANs)

Zhang,

Xie,

Zhuang

et al. 2024

JAIGS

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This paper introduces the application of generative adversarial networks (GANs) in image processing and optimization. GANs model can generate realistic images by co-training generator and discriminator, and achieve remarkable results in image restoration tasks. CATGAN and DCGAN are two commonly used GAN models applied to image classification and image restoration respectively. In addition, the global and local image patching methods can effectively fill the missing areas in the image and show good results in the restoration of large images. In conclusion, the image processing and optimization method based on GANs has shown great potential in practice and provides beneficial insight for future research and application in the field of image processing.

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“…For example, in CT reconstruction, [22]FBP (Filtered back projection) is a more common algorithm in analytical methods. In FBP, the projection data is processed by FFT filtering, and the [23]3D image is reconstructed along the projection direction by the multiplication of the system weight matrix and projection data. The CT projection reconstruction model is shown in Figure 3.…”

Section: Image Reconstructionmentioning

confidence: 99%

GPU-Optimized Image Processing and Generation Based on Deep Learning and Computer Vision

Lin,

Li,

et al. 2024

JAIGS

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In recent years, deep learning has become a core technology in many fields such as computer vision. The parallel processing capability of GPU, greatly accelerates the training and inference of deep learning models, especially in the field of image processing and generation. This paper discusses the cooperation and differences between deep learning and traditional computer vision technology and focuses on the significant advantages of GPU in medical image processing applications such as image reconstruction, filter enhancement, image registration, matching, and fusion. This convergence not only improves the efficiency and quality of image processing, but also promotes the accuracy and speed of medical diagnosis, and looks forward to the future application and development of deep learning and GPU optimization in various industries.

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Application of Machine Learning Algorithm Based on Big Data

Cited by 5 publications

References 8 publications

Utilizing Data Science and AI for Customer Churn Prediction in Marketing

Utilizing Data Science and AI for Customer Churn Prediction in Marketing

Image Processing and Optimization Using Deep Learning-Based Generative Adversarial Networks (GANs)

GPU-Optimized Image Processing and Generation Based on Deep Learning and Computer Vision

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