检索结果-内蒙古大学图书馆

3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Logeswaran, K. Suresh, P. Savitha, S. Sah, Swati Kr, Prasanna Kumar Rajadevi, R. Vasugi, M. Sujit, B. Akilesh, T. Kongu Engineering College Center of Excellence in Real-Time Analytic Department of Artificial Intelligence Tamilnaud Erode India Vellore Institute of Technology Department of Database Systems Tamil Nadu Vellore India K.S.R College of Engineering Department of Computer Science and Engineering Tamil Nadu Tiruchengode India Sharda University School of Engineering and Technology Uttar Pradesh Greater Noida India Kongu Engineering College Department of Information Technology Tamilnaud Erode India Kongu Engineering College Department of Artificial Intelligence Tamilnaud Erode India

ISBN: (纸本)9798331523893

Ensuring the development of high-quality leather products in the industrial sector requires a strong focus on leather quality assurance. In this study, a unique method for enhancing leather quality assurance using Convolutional Neural Networks (CNNs) is presented. Our suggested solution is able to detect and classify leather defects with amazing accuracy and efficiency by utilizing deep learning. CNN integration for leather fault categorization and detection offers a more reliable and affordable solution, ensuring better product quality and satisfied customers. Existing methods for inspecting leather products often suffer from inconsistent quality control, subjective manual inspection processes, and the complexity of accurately identifying and classifying various types of leather defects. By addressing these challenges, the proposed system aims to empower leather manufacturers with a reliable and efficient solution for ensuring consistent product quality and reducing production costs. Convolutional Neural Networks (CNNs) are integrated to provide a highly automated and effective solution that overcomes these scaling challenges. The proposed CNN-based method uses deep learning algorithms to decode images, which allows the system to swiftly and precisely analyse enormous volumes of visual data. To make sure the CNN can differentiate between various defect classes and keep a low rate of false positives and false negatives, its accuracy, precision, recall, and F1 score should be carefully assessed. © 2025 IEEE.

关键词： Fault tolerance

来源：评论

学校读者我要写书评

暂无评论

CMANet: A CNN-Mamba aggregation network for face super-resolution

引用

Pattern Recognition 2025年 168卷

作者： Wu, Ziyi Lu, Tao Zhang, Yanduo Chai, Xiaoyu Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan China Computer School Hubei University of Arts and Science Xiangyang China National Engineering Research Center for Multimedia Software Wuhan University Wuhan China

Currently, most face super-resolution (FSR) methods rely on convolutional neural networks (CNNs) or Transformers, both of which have inherent limitations: CNNs suffer from a limited receptive field, while Transformers introduce quadratic computational complexity due to self-attention. In recent years, Mamba has effectively addressed these challenges using the state space model (SSM), excelling in global modeling. However, due to the highly structured nature of face images, relying solely on global modeling is insufficient for high-quality reconstruction. It is crucial to preserve global structural information while enhancing local texture details. While Mamba excels in global modeling, it lacks the ability to effectively extract local details. To eliminate this issue, we propose the CNN-Mamba Aggregation Network (CMANet) for FSR, which consists of two branches: a CNN branch and a Mamba branch. The CNN branch is a traditional end-to-end FSR network, in which the proposed Multi-Scale Residual Block (MSRB) extracts multi-scale local details. The Mamba branch, the core of the network, is built upon a UNet architecture. Its central component is a carefully designed Dual Scan Mamba Block (DSMB), which combines the Dual Scan Mamba Module (DSMM) and Feature Refinement Feed-Forward Network (FR-FFN). The DSMM enhances the global modeling capabilities of the SSM in both plane and channel scanning directions, while the FR-FFN refines the expressiveness and adaptability of the features. Finally, CMANet reconstructs high-fidelity face images by aggregating features from both branches. Extensive experimental results show that our model significantly outperforms many advanced methods in both objective metrics and subjective image quality. © 2025 Elsevier Ltd

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

The Future with Advanced Analytics: A Sequential Analysis of the Disruptive Technology’s Scope 20th

The Future with Advanced Analytics: A Sequential Analysis of...

引用

20th International Conference on Hybrid Intelligent Systems, HIS 2020 and 12th World Congress on Nature and Biologically Inspired Computing, NaBIC 2020

作者： Varsha, R. Nair, Siddharth M. Tyagi, Amit Kumar Aswathy, S.U. RadhaKrishnan, R. School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus ChennaiTamil Nadu600127 India Center for Advanced Data Science Vellore Institute of Technology ChennaiTamilnadu600127 India School of Computer Science and Engineering Vellore Institute of Technology ChennaiTamilnadu600127 India Department of Computer Science Jyothi Engineering College ThrissurKerala India Department of Civil Engineering Mangakam College of Engineering Kottayam India

ISBN: (纸本)9783030730499

Today every application like e-healthcare, agriculture, etc., is connected through smart devices to reduce workforce and enhance productivity. Many applications like defences, banking an utilities, media and entertainment, transportation, banking, retail, agriculture, education, manufacturing, etc., are using smart devices in their working-structure to improved growth of a business/production. These applications are generating a lot of data, which called as "big data" and this data is increasing at a huge rate. For example, most of the data (90%) is generated in last decade only. Together this, we required modern tools to analyses this data for generating useful results. But in near future, this analytics process may shift towards automation. How these automated analytics by deep learning (by robots/machines) will change future forever. Also, with this automated/advanced analytics process we need to provide a disruptive environment which is more towards to protecting nature. This article provide detail explanation regarding "how machines can be useful in learning process through its automate learning process" and "how machine/Artificial Intelligence (AI) can be useful in detecting vulnerabilities/intrusion without much human interaction instantly" and so on. In 21st century, most of tasks will be completed by machines or artificial intelligences. This work discusses several useful terms, scenarios (with many examples in several applications), tools, open issues with opportunities towards automated analytics, i.e., with discussing that "How AI will change near future". © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Big data

来源：评论

学校读者我要写书评

暂无评论

Biometric Authentication in Smart Cities Using Mobile Cloud Computing

Biometric Authentication in Smart Cities Using Mobile Cloud ...

引用

Advancement in Electronics & Communication engineering (AECE), International Conference on

作者： Ramiz Salama Sinem Al-Turjman Chadi Altrjman Fadi Al-Turjman R.Om Prakash Satya Prakash Yadav Satvik Vats Department of Computer Engineering AI and Robotics Institute Research Center for AI and IoT. Near East University Nicosia TURKIYE Artificial Intelligence Engineering Dept AI and Robotics Institute Near East University Nicosia Turkey Department of Chemical Engineering Waterloo University ON Canada Noida Instue Engineering and Technology Greator Noida U.P India Department of Computer Science and Engineering G.L. Bajaj Institute of Technology and Management(GLBITM) Greater Noida India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India

Smart cities are cities that are designed to be more efficient, sustainable, and connected. As our cities grow and become more complex, it's important to find new ways to address the challenges that arise, such as security, privacy, and mobility. This is where mobile cloud computing and biometric authentication come in. Mobile cloud computing is a way to provide cloud computing services to mobile devices, which means people can access services and data from anywhere and at any time. Biometric authentication is a method of identifying people based on their unique physical or behavioral characteristics, like fingerprints, facial recognition, or voice recognition. These two technologies can be combined to create a secure and personalized environment for residents and visitors in smart cities. By using mobile devices, IoT devices, wireless sensor networks, and edge computing, we can collect real-time data that can be used for big data analytics and machine learning. This means we can optimize city services, like traffic management, waste management, or energy consumption, and enhance the quality of life for people living in smart cities. However, the use of these technologies also poses significant security and privacy risks, so it's important to design systems that are secure and privacy-preserving. This requires interdisciplinary research and collaboration between experts in different fields. In summary, mobile cloud computing and biometric authentication have the potential to transform smart cities by creating a more secure and personalized environment for residents and visitors. But we must also be mindful of the potential risks and work together to address them.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Affective Image Filter: Reflecting Emotions from Text to Images

Affective Image Filter: Reflecting Emotions from Text to Ima...

引用

International Conference on computer Vision (ICCV)

作者： Shuchen Weng Peixuan Zhang Zheng Chang Xinlong Wang Si Li Boxin Shi National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing Academy of Artificial Intelligence School of Artificial Intelligence Beijing University of Posts and Telecommunications

Understanding the emotions in text and presenting them visually is a very challenging problem that requires a deep understanding of natural language and high-quality image synthesis simultaneously. In this work, we propose Affective Image Filter (AIF), a novel model that is able to understand the visually-abstract emotions from the text and reflect them to visually-concrete images with appropriate colors and textures. We build our model based on the multi-modal transformer architecture, which unifies both images and texts into tokens and encodes the emotional prior knowledge. Various loss functions are proposed to understand complex emotions and produce appropriate visualization. In addition, we collect and contribute a new dataset with abundant aesthetic images and emotional texts for training and evaluating the AIF model. We carefully design four quantitative metrics and conduct a user study to comprehensively evaluate the performance, which demonstrates our AIF model outperforms state-of-the-art methods and could evoke specific emotional responses from human observers.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Transcriptomic insights unveil the crucial roles of cytochromes, NADH, and pili in Ag(I) reduction by Geobacter sulfurreducens

引用

Chemosphere 2024年 358卷 142174页

作者： Liu, Chunmao Guo, Dongchao Wen, Su Dang, Yan Sun, Dezhi Li, Pengsong Beijing Key Lab for Source Control Technology of Water Pollution College of Environmental Science and Engineering Beijing Forestry University Beijing100083 China Engineering Research Center for Water Pollution Source Control & Eco-remediation College of Environmental Science and Engineering Beijing Forestry University Beijing100083 China School of Computer Science Beijing Information Science and Technology University Beijing100101 China

Silver (Ag) is a pivotal transition metal with applications in multiple industries, necessitating efficient recovery techniques. Despite various proposed methods for silver recovery from wastewaters, challenges persist especially for low concentrations. In this context, bioreduction by bacteria like Geobacter sulfurreducens, offers a promising approach by converting Ag(I) to Ag nanoparticles. To reveal the mechanisms driving microbial Ag(I) reduction, we conducted transcriptional profiling of G. sulfurreducens under Ag(I)-reducing condition. Integrated transcriptomic and protein-protein interaction network analyses identified significant transcriptional shifts, predominantly linked to c-type cytochromes, NADH, and pili. When compared to a pilus-deficient strain, the wild-type strain exhibited distinct cytochrome gene expressions, implying specialized functional roles. Additionally, despite a down-regulation in NADH dehydrogenase genes, we observed up-regulation of specific downstream cytochrome genes, highlighting NADH's potential role as an electron donor in the Ag(I) reduction process. Intriguingly, our findings also highlight the significant influence of pili on the morphology of the resulting Ag nanoparticles. The presence of pili led to the formation of smaller and more crystallized Ag nanoparticles. Overall, our findings underscore the intricate interplay of cytochromes, NADH, and pili in Ag(I) reduction. Such insights suggest potential strategies for further enhancing microbial Ag(I) reduction. © 2024 Elsevier Ltd

关键词： Proteins

来源：评论

学校读者我要写书评

暂无评论

L-CoIns: Language-based Colorization With Instance Awareness

L-CoIns: Language-based Colorization With Instance Awareness

引用

Conference on computer Vision and Pattern Recognition (CVPR)

作者： Zheng Chang Shuchen Weng Peixuan Zhang Yu Li Si Li Boxin Shi School of Artificial Intelligence Beijing University of Posts and Telecommunications National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University National Engineering Research Center of Visual Technology School of Computer Science Peking University International Digital Economy Academy

Language-based colorization produces plausible colors consistent with the language description provided by the user. Recent studies introduce additional annotation to prevent color-object coupling and mismatch issues, but they still have difficulty in distinguishing instances corresponding to the same object words. In this paper, we propose a transformer-based framework to automatically aggregate similar image patches and achieve instance awareness without any additional knowledge. By applying our presented luminance augmentation and counter-color loss to break down the statistical correlation between luminance and color words, our model is driven to synthesize colors with better descriptive consistency. We further collect a dataset to provide distinctive visual characteristics and detailed language descriptions for multiple instances in the same image. Extensive experiments demonstrate our advantages of synthesizing visually pleasing and description-consistent results of instance-aware colorization.

关键词：

来源：评论

学校读者我要写书评

暂无评论

IoT-based Efficient Waste Management on University Campus: Enhancing Monitoring and Optimization through Machine Learning

IoT-based Efficient Waste Management on University Campus: E...

引用

3rd International Conference on Machine Learning and Data engineering, ICMLDE 2024

作者： Sumathi, M. Hanumanthappa, J. Santhosh Kumar, K.S. Shiva Prakash, S.P. Meghana, J. Department of Studies in Computer Science University of Mysore Manasagangothri Campus Mysuru Karnataka570006 India Department of Artificial Intelligence and Machine Learning Mysore University School of Engineering University of Mysore Manasagangothri Campus Mysuru Karnataka570006 India Department of Information Science and Engineering & Computer Science and Business Systems JSS Science and Technology University Mysuru Karnataka570006 India Department of Computer Science Mahajana Post Graduate Center Mysuru Karnataka570016 India

The introduction of an IoT-based smart waste management system is proposed to optimize various aspects of university campus waste recycle management processes, such as garbage collection scheduling, waste type identification, and collection routes. This system utilizes advanced data analytics algorithms, wireless communication protocols, and sensors to measure and document variations in trash levels, enabling real-time monitoring of waste accumulation. Through this IoT-based infrastructure, university administrators and waste management staff gain fine-grained visibility and control over garbage collection processes. The implications for sustainability advocacy and environmental stewardship in academic environments are significant. By fostering a culture of environmental awareness and accountability, universities can instill ideals of conservation and resourcefulness in their student populations and staff. The proposed IoT-enabled waste bin system exemplifies the synergy between technological innovation and environmental responsibility, serving as a model for institutions worldwide. Collaboration among universities can leverage IoT technology to drive positive change, mitigate environmental degradation, and promote sustainable practices beyond campus boundaries. Efficient waste management is crucial for nurturing sustainability on college and university campuses, as evidenced by the urgent need for the collection and recycling of compost, general waste, and recyclables. Our models consistently achieve high performance, with perfect scores (1.000) across all split ratios, indicating flawless classification of waste types. Although the Artificial Neural Network (ANN) demonstrates slightly lower accuracy, ranging from 0.973 to 0.997 depending on data splits, it still performs well. As a result, Naive Bayes produces less accurate results, typically surpassing 0.93 but falling short of Random Forest, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM)'s flawless

关键词： Electronic Waste

来源：评论

学校读者我要写书评

暂无评论

I/Q Reconstruction of 60 GHz Six - Port Outputs for Phase Differences Detection in Radar Systems

I/Q Reconstruction of 60 GHz Six - Port Outputs for Phase Di...

引用

Asia-Pacific Conference on Microwave

作者： Erfansyah Ali Sung-Nien Hsieh A. A. Pramudita Tjahjo Adiprabowo Ding-Bing Lin Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan University Center of Excellence for Intelligent Sensing-IoT Telkom University Bandung Indonesia School of Engineering Universitas Langlangbuana Bandung Indonesia

ISBN: (数字)9798350363548

ISBN: (纸本)9798350363555

This paper presents an I/Q reconstruction of a six-port that is designed using Substrate Integrated Waveguide (SIW). The designed six-port has good insertion loss, transmission loss, and phase difference detection. The measurement of six-port was done by multiple two-port measurements using the Network Analyzer and reconstructed into a complete scattering matrix. The reconstructed I/Q plot and phase difference linearity show good results at an operating frequency of 60 GHz and are promising for detecting phase differences in radar systems.

关键词： Transmission line matrix methods Radar detection Scattering Radar Insertion loss Phase frequency detectors Network analyzers Propagation losses Loss measurement Substrates

来源：评论

学校读者我要写书评

暂无评论

Edge AI-Enabled Chicken Health Detection Based on Enhanced FCOS-Lite and Knowledge Distillation

arXiv

引用

arXiv 2024年

作者： Tong, Qiang Wang, Jinrui Yang, Wenshuang Wu, Songtao Zhang, Wenqi Sun, Chen Xu, Kuanhong Research & Development Center China SONY Group Corporation Beijing China School of Computer Science Beijing University of Posts and Telecommunications Beijing China School of Electrical and Electronic Engineering Nanyang Technological University Singapore

Edge-AI based AIoT technology in modern poultry management has shown significant advantages for real-world scenarios, optimizing farming operations while reducing resource requirements. To address the challenge of developing a highly accurate edge-AI enabled detector that can be deployed within memory-constrained environments, such as a highly resource-constrained edge-AI enabled CMOS sensor, this study innovatively develops an improved FCOS-Lite detector as a real-time, compact edge-AI enabled detector designed to identify chickens and assess their health status using an edge-AI enabled CMOS sensor. The proposed FCOS-Lite detector leverages MobileNet as the backbone to achieve a compact model size. To mitigate the issue of reduced accuracy in compact edge-AI detectors without incurring additional inference costs, we propose a gradient weighting loss function for classification and introduce a CIOU loss function for localization. Additionally, a knowledge distillation scheme is employed to transfer critical information from a larger teacher detector to the FCOS-Lite detector, enhancing performance while preserving a compact model size. Experimental results demonstrate the proposed detector achieves a mean average precision (mAP) of 95.1 % and an F1-score of 94.2 %, outperforming other state-of-the-art detectors. The detector operates efficiently at over 20 FPS on the edge-AI enabled CMOS sensor, facilitated by int8 quantization. These results confirm that the proposed innovative approach leveraging edge-AI technology achieves high performance and efficiency in a memory-constrained environment, meeting the practical demands of automated poultry health monitoring, offering low power consumption and minimal bandwidth costs. © 2024, CC BY-NC-ND.

关键词： CMOS integrated circuits

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：