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2024 international conference on Electronics and Devices, Computational science, ICEDCS 2024

作者： Zhang, Qian Liu, Yijun Huang, Xiaowei Chongqing University of Science and Technology Engineering Research Center Waste Oil Recovery Technology and Equipment Ministry of Education Chongqing Technology and Business University School of Computer Science and Engineering School of Artificial Intelligence Chongqing China Chongqing University of Science and Technology School of Computer Science and Engineering School of Artificial Intelligence Chongqing China

ISBN: (纸本)9798331527624

This platform leverages digital twin technology, focusing on intelligent production as the research subject, aiming to achieve intelligent comprehensive evaluation and supervision of the pork production process. Through digital twin modeling, a three-level performance evaluation model is constructed, encompassing model mapping, behavior mapping, and state mapping, to comprehensively reflect pork production capacity. To address imbalances in the production process, the system introduces an intelligent supervision module, utilizing elastic time windows and dynamic flow matching methods to enhance the robustness of the monitoring system. The entire system includes a big data platform based on 5G transmission technology, an intelligent pig farming model, and a digital twin-based intelligent pork production model, forming a multi-scale collaborative intelligent analysis platform that provides scientific support for improving pork production efficiency. © 2024 IEEE.

关键词： Smart manufacturing

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Utilizing Customized 3D U-Net Framework for the Classification and Segmentation of multi-regional Brain Tumors in Volumetric MRI Images 3

Utilizing Customized 3D U-Net Framework for the Classificati...

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3rd international conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Titu, Md. Minhaj Taher Mary, Mekhala Mariam Ahamed, Md. Faysal Oishee, Tasfia Mashud Hasan, Md Mahadi International Standard University Department of Computer Science & Engineering Dhaka1212 Bangladesh Rajshahi University of Engineering & Technology Department of Electrical & Computer Engineering Rajshahi6204 Bangladesh International Standard University Department of Computer Science and Engineering Dhaka1212 Bangladesh

ISBN: (纸本)9798350388282

Precise diagnosis and treatment planning depend heavily on the segmentation and classification of brain tumors in medical pictures. The diagnostic and therapeutic challenges posed by brain tumors necessitate innovative solutions within the domain of medical imaging. This research endeavors to address this pressing need through the introduction of a pioneering approach to multi-regional tumor classification and segmentation. Utilizing volumetric MRI data, our study introduces a bespoke 3D U-Net framework meticulously tailored to the intricacies of brain tumor analysis. Our framework offers holistic solutions to two foundational objectives: classification and segmentation. In the classification facet, we strive to discriminate between various brain tumor types. Concurrently, our segmentation component aspires to precisely delineate tumor boundaries and partition distinct tumor regions. We used the BraTS 2020 dataset, and dice and Jaccard coefficient functions were employed in the model optimization process. The custom-tailored architecture of 3D U-Net, purpose-built for the nuances of brain tumor imaging, ensures resilient performance while preserving vital spatial information essential for accurate tumor delineation. Leveraging deep learning methodologies and a comprehensive dataset of volumetric MRI scans, our model showcases outstanding accuracy in classifying diverse tumor types and in the precise segmentation of tumor regions with a dice score are 88.1%, 79.4%, and 76.0% for WT dice, TC dice and ET dice. And Jaccard Scores are 79.4% for WT Jaccard, 68.2% for TC Jaccard and 64.3% for ET Jaccard. The proposed framework holds the potential to alleviate subjectivity in diagnostic processes, enhance the precision of treatment planning, and ultimately ameliorate patient outcomes. © 2024 IEEE.

关键词： Tumors

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Exploration of an Intelligent Research Support Platform for Environmental Monitoring Big Data Based on Data Fusion Technology 7

Exploration of an Intelligent Research Support Platform for ...

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7th international conference on computer Information science and Application Technology, CISAT 2024

作者： Zhang, Yi Liu, Yongming Guangdong Polytechnic of Environmental Protection Engineering Guangdong China

ISBN: (纸本)9798350375107

At present, China is comprehensively promoting the construction of environmental big data platform, but there are few to explore the construction of scientific research cloud platform from the perspective of supporting environmental scientific research. Based on the review of the research status of environmental big data platform at home and abroad and the investigation of environmental big data research support platform, it is concluded that the research platform should have the main functions of supporting multi-source heterogeneous data integration, interactive cooperation experiment and data visualization. Based on the concept of XaaS, the framework of Environmental monitoring is constructed. The key technologies needed for the platform include multi-source heterogeneous data integration technology, Web service technology and data visualization and human-computer interaction technology. Huanyuan big data scientific research cloud platform developed under this framework has shown certain results. © 2024 IEEE.

关键词： Cloud platforms

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Comparative Study of Communication Protocols for Vehicle-Based Networks 4

Comparative Study of Communication Protocols for Vehicle-Bas...

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

作者： George, Remya P. Ahmad, Nazia Hussain, Sajithunisa Khan, Rubina Liyakat Quraishi, Suhail Javed Singh, Anupam Applied College Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Mriirs Haryana Faridabad India Graphic Era Hill University Department of Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Deemed to be University Department of Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350387490

VANETs are an emerging area of interest in wireless communications mainly because of the features like high speed and mobility and dynamic topology. The current study presents a comprehensive evaluation of multi-variant routing protocol adapted for VANETs, wherein based on the protocol's behavior they are categorized by topology based, geographic based, broadcast based, and cluster based. These protocols are assessed based on parameters such as latency, loss packet ratio, and overhead. Additionally, we outline their practical implications and the directions for further research. © 2024 IEEE.

关键词： Authentication Protocol

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Surface Defect Detection Algorithm for Strip Steel Based on Improved YOLOv7 Model

IAENG International Journal of Computer Science

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IAENG international Journal of computer science 2024年第3期51卷 308-316页

作者： Wang, Zhu Liu, Weisheng the College of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China the College of Computer Science and Software Engineering the University of Science and Technology Liaoning CO Anshan114051 China

This research proposes a refined deep learning framework aimed at boosting the precision and efficacy of detecting surface imperfections in strip steel. This method integrates enhancement and simplification techniques inspired by the You Only Look Once version 7 (YOLOv7) detection method, resulting in significant enhancements in the model's accuracy, speed, and flexibility. The substitution of ELAN with Bottleneck Transformer 3 (BoT3) leads to improved accuracy and mean Average Precision (mAP) values, while also introducing a more lightweight network architecture. The incorporation of the Involution mechanism enhances the model's feature extraction capabilities, thereby improving its ability to recognize small targets through the utilization of local perceptual fields. The ASPP_CA architecture leverages a multi-scale feature fusion technique along with an attention mechanism to reduce model parameters and enhance inference speed. Furthermore, it extends the model's receptive field, allowing it to capture additional visual information. The enhanced algorithm, denoted as YOLOv7-IBA, demonstrates empirical results that underscore its superiority over the three current state-of-the-art detection techniques in identifying surface flaws on strip steel. The accuracy has been improved to 82.9%, representing a significant increase of 7.2% compared to the previous performance. Furthermore, the mean mAP value has experienced a 3.2% increase, reaching a total of 79.9%. Moreover, there has been a remarkable 8.8% improvement in efficiency. The adoption of this approach holds the potential to enhance both the precision and productivity of strip surface flaw detection, while also providing valuable methodological support for the advancement of other related disciplines. © (2024), (international Association of Engineers). All Rights Reserved.

关键词： Object detection

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Adaptive Scale Selection Network for Crowd Counting

Adaptive Scale Selection Network for Crowd Counting

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2023 international conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Lai, Ting-Hsu Liu, Tsung-Jung Liu, Kuan-Hsien National Chung Hsing University Dept. of Electrical Engineering Taiwan National Taichung University of Science and Technology Dept. of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798350324174

Crowd counting is a computer vision task that focuses on accurately estimating the number of people present in a given scene. In the past few years, convolutional neural network-based deep learning techniques have achieved remarkable success in many computer vision tasks, including crowd counting. In the field of crowd counting, large-scale changes have always been a great challenge. To resolve this problem, previous work used multiple branches to obtain information at different scales and combined it. However, purely combining multi-branch features cannot effectively utilize multi-scale information. In this work, we modify the previous multi-branch architecture, which can reasonably select the appropriate scale information. Furthermore, we test our model on the ShanghaiTech dataset and demonstrate the competitive performance of our method. © 2023 IEEE.

关键词： computer vision

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A multi-exposure image fusion method based on histogram matching and convolutional neural network

A multi-exposure image fusion method based on histogram matc...

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2023 international conference on computer Graphics, Artificial Intelligence, and Data Processing, ICCAID 2023

作者： Zhu, Xinli Zhang, Yasheng Fang, Yuqiang Luo, Di Space Engineering University Beijing101416 China

ISBN: (纸本)9781510677685

For high dynamic range image reconstruction in dynamic scenes, this paper proposes a multi-exposure sequence image fusion method based on histogram matching and convolutional neural network. First, image alignment is performed on the reference image based on the histogram matching method. Then, a multi-exposure fusion model based on convolutional neural network is constructed for the aligned multi-exposure sequence images. Finally, the fusion model is trained based on the training data, and the multi-exposure sequence image fusion in dynamic scenes is realized. The experimental results show that the method proposed in this chapter effectively avoids the influence of dynamic scenes on the fusion of multi-exposure sequence images, and at the same time can generate high-quality fused images. © 2024 SPIE.

关键词： Image fusion

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An Efficient Convolutional multi-Scale Vision Transformer for Image Classification 2

An Efficient Convolutional Multi-Scale Vision Transformer fo...

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2nd international conference on Image Processing, computer Vision and Machine Learning, ICICML 2023

作者： Zhang, Ji Chen, Zhihao Ge, Yiyuan Yu, Mingxin Beijing Information Science and Technology University School of Instrument Science and Opto-Electronics Engineering Beijing China Beijing Information Science and Technology University Computer School Beijing China Jilin University Department of Software Engineering Changchun China

ISBN: (纸本)9798350331417

This paper introduces an innovative and efficient multi-scale Vision Transformer (ViT) for the task of image classification. The proposed model leverages the inherent power of transformer architecture and combines it with the concept of multi-scale processing generally used in convolutional neural networks (CNNs). The work aims to address the limitations of conventional ViTs which typically operate at a single scale, hence overlooking the hierarchical structure in visual data. The multi-scale ViT enhances classification performance by processing image features at different scales, effectively capturing both low-level and high-level semantic information. Extensive experimental results demonstrate the superior performance of the proposed model over standard ViTs and other state-of-the-art image classification methods, signifying the effectiveness of the multi-scale approach. This research opens new avenues for incorporating scale-variance in transformer-based models for improved performance in vision tasks. © 2023 IEEE.

关键词： Classification Convolutional neural networks multi-scale Vision Transformer

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international conference computer science and engineering, IC2SE 2019

International Conference Computer Science and Engineering, I...

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1st international conference computer science and engineering, IC2SE 2019

The proceedings contain 133 papers. The topics discussed include: prediction of canal erosion on tidal swamp delta Telang I, Banyuasin Regency, South Sumatra;assessment for seismic activities in Pesisir Selatan West Sumatra in 2018;shallow well water salinity viewed from distance of well to coastline and ground water level elevation in Purus Padang Village;Bandit algorithms in information retrieval evaluation and ranking;a review study: the effect of face aging at estimating age and face recognition;and students' problem-solving ability in temperature and heat concepts.

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Hyperparameter Selection in Support Vector Regression Using Difference of Convex Algorithms-Based Methods 4

Hyperparameter Selection in Support Vector Regression Using ...

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4th international conference on Electronic Information engineering and computer science, EIECS 2024

作者： Su, Buda Qian, Yaru School of Computer Science and Information Engineering Hohhot Minzu College Hohhot China School of Electronics & Computer Science University of Southampton Southampton United Kingdom

ISBN: (纸本)9798331531409

Support Vector Regression (SVR) operates as an effective regression methodology within the machine learning landscape, with its performance largely dependent on hyperparameter tuning. This study proposes a bilevel optimization schema tailored for hyperparameter selection in SVR, subsequently recasting the problem into a Difference of Convex functions model (DC). We delineate convergence outcomes for this approach. Through computational experiments, the efficacy of this methodology is demonstrated, surpassing traditional hyperparameter selection techniques. © 2024 IEEE.

关键词： Support vector regression

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