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Twin Bounded Least Squares Support Vector Regression 6th

Twin Bounded Least Squares Support Vector Regression

引用

6th IFIP TC 12 international conference on Intelligence Science, ICIS 2024

作者： Chen, Ran Liu, Muhan Ma, Jinwen Department of Information and Computational Sciences School of Mathematical Sciences Peking University Beijing100871 China Department of Applied Mathematics China Agricultural University Beijing100083 China

ISBN: (纸本)9783031712524

Support Vector Machine (SVM) has received much attention in machine learning due to its profound theoretical research and practical application results. Support Vector Regression (SVR) has become a powerful tool for solving regression problems. Least Squares Support Vector Regression (LSSVR) has advantages in computing speed but can be prone to overfitting due to its sensitivity to noise and outliers. Additionally, Twin Support Vector Regression (TSVR) shows insufficient flexibility when dealing with large-scale data, and its robustness to noise could be improved. In response to these problems, this paper proposes an innovative solution: Twin Bounded Least Squares Support Vector Regression (TBLSSVR). This model combines the advantages of LSSVR and TSVR and introduces regularization terms to mitigate their limitations effectively. The regularization term helps minimize structural risk, reflecting the advantages of statistical learning theory and ensuring the stability of the solution. Experimental results demonstrate that TBLSSVR improves regression accuracy and significantly accelerates the solution speed, providing new directions and methods for future technology development. © IFIP international Federation for information Processing 2025.

关键词： Vectors

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A Grouping Strategy-Based Progressive Fusion Network for Hyperspectral Image Super-Resolution

A Grouping Strategy-Based Progressive Fusion Network for Hyp...

引用

2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Lv, Guohua Zhang, Baodong Gao, Yongbiao Zhao, Guixin Wang, Guotao Wang, Juncan Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Shandong Key Laboratory of Ubiquitous Intelligent Computing Jinan China Ministry of Education China Beihang University Beijing China

ISBN: (纸本)9798350368741

Hyperspectral super-resolution involves combining low-resolution hyperspectral images with high-resolution multispectral images to produce a high-resolution hyperspectral image. Recently, although many methods for hyperspectral image super-resolution have been proposed, they often fail to fully utilize the high similarity among adjacent bands to enhance fusion performance. Therefore, we propose a grouping strategy-based progressive fusion network (GPFNet) for hyperspectral super-resolution. The core of GPFNet is the grouping strategy fusion block (GPF block), in which grouping-based spatial-spectral information fusion and spatial information refinement are performed. We design the spatial-spectral information fusion module (SSIFM) based on grouped convolutions to capture the feature differences from adjacent bands. To refine spatial details, we develop the spatial information enhancement module (SpaEM), which leverages the hierarchical features extracted by the multi-scale feature extraction module (MIEM). Additionally, a progressive fusion strategy, which involves using multiple upsampled hyperspectral images and downsampled multispectral images, further preserves spectral integrity and spatial details. Extensive experiments show that GPFNet outperforms state-of-the-art methods both qualitatively and quantitatively. © 2025 IEEE.

关键词： group convolution Hyperspectral image image fusion super-resolution

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Model Optimization for Personalized Health Metrics Analysis 5

Model Optimization for Personalized Health Metrics Analysis

引用

5th international conference on Advanced Research in computing, ICARC 2025

作者： Perera, Madhuranga Wijesiriwardena, Amasha Pathirana, Amandi Gamaathige, Lakshan Wijesiri, Pipuni Jayakody, Anuradha Sri Lanka Institute of Information Technology Dept of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798331530983

This paper investigates the development and application of four machine learning models designed to enhance personalized health management, specifically targeting young adults aged 15-30. The research addresses common health challenges, such as obesity and lifestyle-induced diseases, through data-driven methodologies that provide personalized meal plans, workout recommendations, and progress monitoring. The first model generates optimized personalized recommendations according to the user's health condition using Random Forest and Decision Tree algorithms. The second model utilizes an ensemble of Random Forest, LightGBM, and XGBoost, combined through a stacking technique with Linear Regression as the meta-model, to generate optimized personalized meal plans according to health condition. The third model generates optimized workout plans using Gradient Boosting and XGBoost classifiers, accounting for individual fitness objectives, body compositions, and medical conditions. A fourth model predicts goal achievement timelines by analyzing features such as caloric balance and hydration efficiency, providing users with actionable feedback using XGBoost. The integration of these AI-driven components into a scalable digital platform demonstrates the potential of machine learning in transforming health management. Future enhancements include improving model accuracy, enabling real-time feedback, and deploying the system as an accessible mobile application. © 2025 IEEE.

关键词： Nutrition

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Sub-Batch Update Mechanism for Component-Wise Natural Gradient Descent 24

Sub-Batch Update Mechanism for Component-Wise Natural Gradie...

引用

8th international conference on Advances in Artificial Intelligence, ICAAI 2024

作者： Van Tran, Sang Nakata, Toshiyuki Yamaguchi, Rie Shigetomi Mhd, Irvan Yoshimoto, Yoshihide Graduate School of Information Science and Technology The University of Tokyo Bunkyo Tokyo Japan

ISBN: (纸本)9798400718014

Neural networks have become a cornerstone of modern machine learning, powering advances in numerous domains. In distributed computing environments, leveraging large batch sizes is crucial for the efficient training of these networks. However, this approach often leads to reduced data stochasticity when sampling information such as the loss function gradient, adversely affecting the generalization performance of the models. We tackle this issue by introducing the Sub-Batch update mechanism, which divides the large batch into sub-batches and updates the network parameters using each sub-batch. The Sub-Batch update mechanism increases the number of nonlinear operations of calculating the neural network parameter update vector from the sampled network information in Component-Wise Natural Gradient Descent (CW-NGD) neural network training algorithm. This increases the data stochasticity, resulting in accuracy improvement in the trained neural network. Experimental results show that with Sub-Batch update mechanism applied CW-NGD produces trained networks with higher accuracy compared to Adam, SGD, across all six datasets tested, including the ImageNet dastaset. © 2024 Copyright held by the owner/author(s).

关键词： Deep neural networks

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Enhancing the Compressed Video Quality through a Neural Network Approach 4

Enhancing the Compressed Video Quality through a Neural Netw...

引用

4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Panneerselvam, Karthick Rajalakshmi, K. Sornalatha, P. Kpr Institute of Engineering and Technology Department of Computer Science & Engineering Tamil Nadu Uthupalayam641407 India Cmr Institute of Technology Department of Mca Karnataka Bengaluru560037 India Alagappa University Department of Computer Applications Tamil Nadu Karaikudi630003 India

ISBN: (纸本)9798331523923

Video compression mainly focuses on reducing the file size of videos with little loss of quality, but most video compression algorithms on the market do not focus on clarity. Data compression is increasingly being considered as a necessary issue in larger engineering communities. Data compression using neural networks has been the main topic of some related research. The main objective of this study would be to apply the video in an amazing way with the help of an Enhanced Generative Adversarial Network (EGAN). EGAN was better to manage and produced better consistent results with fewer artifacts than other Neural Networks (NN). Deep neural networks (DNNs), recurrent neural networks (RNNs), generational adversarial networks (GANs), and various autoencoder (AE) variants have all been used for various compression methods. It would be a potential option for incredible movies. Based on significantly reduced sample input films, the proposed neural network was able to maintain high-frequency information and generate pleasurable sensory videos. A Residual Dense Block (RDB) is the sort of block utilized in the design. In comparison to previous state-of-the-art approaches, the comprehensive Mean-Opinion-Score (MOS) test, and the Video Multi-Method Assessment Fusion (VMMAF) test, reveal extremely substantial increases in terms of human experiential accuracy. The researchers are also proposing a new data pre-processing method based on the Deep Recurrent Neural Network (DRNN) that has been combined with two leading-edge coding standards, and the Adaptable Video coding (AVC). The results show consistent encoding benefits across all sequences examined at multiple spatial frequencies, with an average bit rate savings of 4.0 percent relative to AVC. © 2025 IEEE.

关键词： Image coding

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Boot process of RISC-V multi-core heterogeneous SoC integrated with eFPGA 24

Boot process of RISC-V multi-core heterogeneous SoC integrat...

引用

8th international conference on Electronic information technology and Computer Engineering, EITCE 2024

作者： Gao, Po Zhang, Yanxin Tan, Lang Yang, Lixin Niu, Bin Yan, Shengli Li, Dejian Wang, Zhihua Beijing Smartchip Microelectronics Technology Co. Ltd. Beijing China School of Integrated Circuits Tsinghua University Beijing China

ISBN: (纸本)9798400710094

The RISC-V multi-core heterogeneous SOC has five harts that divided into three clusters, and integrates an eFPGA. Inter-cluster is asymmetric-multiprocessor and intra-cluster is symmetric-multiprocessor. The SoC not only has the powerful computing ability of general CPU, but also has the scalability and parallel processing ability of FPGA. But it also makes the process of loading and launching the firmware extremely complicated. According to the hardware architecture of SoC and possible use scenarios, four basic bare-metal and operating system combination boot schemes and three methods to load eFPGA executable files are designed. The actual test shows that the boot process can work as expected. These boot schemes cover almost all the usage scenario of SoC, expand the application range of SoC, and are more conducive to the popularization and use of SoC. © 2024 Copyright held by the owner/author(s).

关键词： Firmware

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Privacy-preserving Bayesian Inference for Sparse Gaussian Processes 24

Privacy-preserving Bayesian Inference for Sparse Gaussian Pr...

引用

8th international conference on Advances in Artificial Intelligence, ICAAI 2024

作者： Nakachi, Takayuki Information Technology Center University of the Ryukyus Okinawa Nishihara Japan

ISBN: (纸本)9798400718014

Edge cloud computing is rapidly gaining traction across various domains, including data analysis. However, concerns regarding privacy breaches due to accidental data leakage have been raised. To address this issue, we propose a privacy-preserving Bayesian inference method specifically designed for sparse Gaussian processes. The proposed method enables Bayesian inference for sparse Gaussian processes while keeping the input data, including inducing points, scrambled through a random unitary transform (RUT). We prove that our Bayesian inference for scrambled data performs the same as Bayesian inference for non-scrambled data. Furthermore, we propose an access control method for inference by changing the private keys of the RUT for training and test data. Finally, we demonstrate the effectiveness of our method through experiments with diabetes data from the medical analysis field. © 2024 Copyright held by the owner/author(s).

关键词： information leakage

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Design and Implementation of an Automatic Scoring Tool for Adaptive Screen Resolutions 24

Design and Implementation of an Automatic Scoring Tool for A...

引用

8th international conference on Electronic information technology and Computer Engineering, EITCE 2024

作者： Guo, Liqing Zhang, Qin Li, Wenyao Yang, Qunwei General Education Center Xiamen Huaxia University Fujian Xiamen China College of Information and Smart Electromechanical Engineering Xiamen Huaxia University Fujian Xiamen China

ISBN: (纸本)9798400710094

Xiamen H University’s adoption of the Future Education System V4.0 for the final assessment of"Fundamentals of Computer Science" implements paperless examinations, reducing the burden on teachers. However, the system has deficiencies in batch automatic correction and student learning status analysis, and does not adapt well to varying screen resolutions. To address these issues, this study utilizes Python to develop an automatic scoring tool for the existing system, designed to adapt to various screen resolutions. The tool employs a layered architecture to create an efficient and stable automatic scoring solution, integrating functions such as parameter setting, automatic scoring, grade analysis, and file renaming. This tool enhances the efficiency and accuracy of marking work, enables a comprehensive assessment of students’ mastery of computer course content, and improves the system’s user-friendliness, providing a new approach to examination and assessment methods in the education field during the digital transformation process. © 2024 Copyright held by the owner/author(s).

关键词： Students

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Research on Image Quality Assessment Methods for Small Target Data Augmentation under the YOLO Framework 24

Research on Image Quality Assessment Methods for Small Targe...

引用

13th international conference on computing and Pattern Recognition, ICCPR 2024

作者： Liu, Zhibin Liu, Pengyu Cui, Bifeng Li, Gang Deng, Jun Li, Qian Department of Information Science Beijing University of Technology Beijing China

ISBN: (纸本)9798400717482

When using image scaling algorithms for data augmentation on small targets within the YOLOv framework, it was observed that the ranking of different image scaling algorithms based on their data enhancement effects significantly diverges from the rankings provided by mainstream image quality assessment methods based on the human visual system. This indicates that the machine vision system of YOLOv5 has different requirements for image quality compared to the human visual system. By analyzing various image quality assessment methods, this study identified an image quality assessment method, BLIINDS-II, that is highly correlated with the data enhancement effects of YOLOv5. It was found that YOLOv5's assessment of image quality is derived from the non-DC coefficients of the image following a Discrete Cosine Transform (DCT), focusing on the high-frequency components of the image. © 2024 Copyright held by the owner/author(s).

关键词： Discrete cosine transforms

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Application Analysis of Artificial Intelligence technology in Comprehensive Energy Service 14th

Application Analysis of Artificial Intelligence Technology i...

引用

14th international conference on Frontier computing, FC 2024

作者： Fu, Dengwei Ira A. Fulton Schools of Engineering School of Computing and Augmented Intelligence Arizona State University TempeAZ United States

ISBN: (纸本)9789819627974

With the growing global demand for energy and the increasingly serious environmental problems, the comprehensive energy services based on artificial intelligence technology, as an efficient, intelligent and sustainable way of energy utilization, has attracted more and more attention. The market participating in the comprehensive energy service platform is a bilateral market dominated by energy suppliers and energy users. On the basis of high-quality power energy resources, a cloud platform for intelligent management of comprehensive energy services is constructed in order to realize the comprehensive integration of energy flow and information flow in the power energy service industry. The platform can provide enterprises and residents with a variety of intelligent services such as energy management, energy forecasting, energy optimization and value-added services, effectively mobilize energy utilization and service levels, efficiently drive the development of local economy, and provide more diverse user communication platforms and information exchange services for power energy services. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Energy development

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