检索结果-内蒙古大学图书馆

2023 IEEE Applied Imagery Pattern Recognition Workshop, AIPR 2023

作者： Li, Yu Das, Rana Duong, Cuong Lim, Teng-Teeh Haithcoat, Timothy Ersoy, Ilker Shyu, Chi-Ren University of Missouri Department of Electrical Engineering and Computer Science ColumbiaMO United States University of Missouri Biological Engineering ColumbiaMO United States University of Missouri Plant Science and Technology Extension ColumbiaMO United States University of Missouri Institute for Data Science and Informatics Columbia United States University of Missouri Institute for Data Science and Informatics Electrical Eng. and Computer Science Dept. ColumbiaMO United States

ISBN: (纸本)9798350359527

Poultry productions have shifted towards larger farms and often cluster in certain regions. However, many of the smaller farms with a considerable amount of production are not considered concentrated animal feeding operation under regulation in many regions, and therefore are not required to obtain a permit. As a result, information of these operations is not being recorded and monitored, contributing to an increased risk for biosecurity issues. The development in computer vision and availability of high-resolution remote sensing imagery allows the possibility for automatic localization and operation estimation of the unregistered farms, filling in the missing information needed for biosecurity risk management. The farm information extracted from images can be incorporated with other available geospatial data, such as road networks, watersheds, etc., to build a comprehensive knowledge base for actionable plans. In the current study, we present a framework for automated localization, analysis, and visualization of poultry farms from remote sensing images. A robust machine learning method was developed to computationally link the visual features from remote sensing imagery to Missouri department of Natural Resources Concentrated Animal Feeding Operation data, to identify the animal farm type, location, and potential capacity in the region. The developed process can be performed automatically on images of different timestamps to analyze temporal and spatial development of poultry production facilities of a region. This research contributes to the effective management and regulation of poultry and potentially other animal farms, providing insights into farm characteristics and capacities while facilitating informed decision-making. © 2023 IEEE.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Intelligent Edge Caching for Metaverse Applications Based on Grover Search

Intelligent Edge Caching for Metaverse Applications Based on...

引用

Artificial Intelligence of Things (AIoT), IEEE Annual Congress on

作者： Guoquan Wu Jianhang Tang Yang Zhang Bo Wang Wenchao Jiang Zehui Xiong Yixiong Feng State Key Laboratory of Public Big Data Guizhou University China Department of Computer Science and Engineering Southern University of Science and Technology China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore

ISBN: (数字)9798350392296

ISBN: (纸本)9798350392302

Metaverse is envisaged as an evolving Internet paradigm that allows people to play, work, and socialize in a shared and virtual ecosystem with immersive and seamless experiences. However, multiple users simultaneously request massive resources for avatar physics emulation and graphical rendering. How to provide high-quality and low-latency meta-verse services for massive concurrent users is a crucial problem. In this work, an intelligent Grover search-based edge caching (GEC) algorithm is proposed for metaverse applications, where quantum theory is used to generate caching solutions with low time complexity. Metaverse scenes can be divided into massive environment panoramic frames and dynamic objects. An edge server is deployed to render and cache the common environment panoramic frames, while the dynamic objects are rendered on head-mounted displays (HMDs). Based on the quantum theory, the panoramic frames are split into several tiles and represented by qubits. The optimal caching results are obtained by performing unitary transformations for the tiles’ features. Finally, we provide extensive simulation experiments by a real- world metaverse dataset. The numerical results reveal that the GEC algorithm can reduce 11.09% of the service time and increase 13.34% of the cache hit rate on average by comparing it with two benchmark caching algorithms.

关键词： Metaverse Heuristic algorithms Simulation Qubit Quantum mechanics Rendering (computer graphics) Internet of Things

来源：评论

学校读者我要写书评

暂无评论

Enhancing Speech Emotion Recognition Combining Silence Elimination and Attention Model with a Novel CNN Architecture 8th

Enhancing Speech Emotion Recognition Combining Silence Elimi...

引用

8th International Conference on Information and Communication technology for Intelligent Systems, ICTIS 2024

作者： Gawali, Piyush P. Takale, Dattatray G. Deshmukh, Gopal B. Kashid, Shraddha S. Mahalle, Parikshit N. Sule, Bipin Ashokrao, Patil Rahul Derle, Deepak R. Department of Computer Engineering Vishwakarma Institute of Information Technology SPPU Pune Pune India Department of Computer Engineering MIT ADT University Pune India Department of Artificial Intelligence and Data Sciences Vishwakarma Institute of Information Technology SPPU Pune Pune India and Humanities Vishwakarma Institute of Technology Pune India Department of Computer Science and Application KTHM College Nashik India

ISBN: (纸本)9789819766772

A natural interface for human–computer interaction is automatic speech emotion recognition, but it encounters challenges in handling non-emotional speech segments, especially silence, since it is non-emotional speech segmentation that requires recognition. Using a carefully designed attention model and a silence elimination approach, this paper proposes a novel method of enhancing emotion recognition performance by combining silence elimination with attention. In the present study, an enhanced Convolutional Neural Network architecture has been proposed, comprising of nine layers, utilizing a combination of convolutional 1D As a result of the research, CNNs have been employed in a revolutionary speech-emotion detection system that reaches an impressive 96.00% accuracy rate. There is great potential in this breakthrough to develop social robots and conversational robots capable of conveying nuanced human emotions. This breakthrough has marked a significant improvement in accuracy compared to prior models based on the same dataset. In addition to the broader classifications of positive, negative, and neutral emotions, the model's accuracy is evaluated across a wide range of emotion classes, such as anger, calmness, fear, happiness, and sadness for both genders. There is considerable evidence to suggest that combined noise cancellation and attention models are more effective than individual models of noise cancellation or attention. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

A new network intrusion detection system based on auto-encoding and variance analysis

A new network intrusion detection system based on auto-encod...

引用

IEEE International Conference on Civil Aviation Safety and Information technology (ICCASIT)

作者： Dequ Chen Ming Zhang Fujian Provincial Key Laboratory of Big Data Mining and Applications/School of Computer Science and Mathematics Fujian University of Technology Fuzhou China

In today's big data era, ensuring the network security of information technology (IT) communication facilities is one of the most challenging issues. In fact, with progress in technology, hackers have developed increasingly complex and risky network attacks, making malicious access identification a very tired task. Under various threats, the existing analysis methods encounter many challenges in detecting and mitigating these illegal accesses. This work presents a new network intrusion detection system (IDS) which combines deep learning method and mathematical strategy. Indeed, our IDS makes full use of data analysis, mathematical modeling, and deep learning to select and optimize features that are more relevant for classification. We use NSL-KDD dataset to test the performance of the IDS. The experimental results show that our proposed method has better performance compared with traditional deep learning, machine learning and recently proposed advanced methods.

关键词：

来源：评论

学校读者我要写书评

暂无评论

RoyaltyChain: A Blockchain-Powered big data Framework for Accurate Music Royalties Prediction

RoyaltyChain: A Blockchain-Powered Big Data Framework for Ac...

引用

IEEE India Conference (INDICON)

作者： Megh H. Shah Parth V. Sheth Aparna Kumari Sudeep Tanwar Vrajesh Chawra Sunil Kumar Department of Computer Science and Engineering Institute of Technology Nirma University Ahmedabad India Department of AI and Data Science Guru Jambheshwar University of Science & Technology Hisar Haryana India

ISBN: (数字)9798350391282

ISBN: (纸本)9798350391299

The growth of the commodification of music in the present age has made royalties allocation in an efficient, straight-forward manner to the stakeholders, in general, a complex issue. To address these challenges, this paper introduces RoyaltyChain, a new framework powered by predictive analytics and blockchain. Using factors like streaming data, social media statistics, and geographical factors, Random Forest Regression is employed to predict royalties in a big data environment, with a blockchain-backed system ensuring secure and transparent distribution. Here Ethereum blockchain-based smart contracts are designed in Remix IDE and the Interplanetary File System (IPFS) is incorporated to handle data storage cost issues in blockchain. The performance evaluation shows an increase in the overall efficiency in predicting outcomes and the high scalability of our system in comparison with the related techniques with respect to accuracy, scalability, etc. RoyaltyChain provides a scalable framework to establish efficient and fair distribution of money from the consumption of music in this increasingly complex world.

关键词： Performance evaluation Accuracy Social networking (online) Scalability Music big data Blockchains Stakeholders InterPlanetary File System Predictive analytics

来源：评论

学校读者我要写书评

暂无评论

Adaptive data-Free Quantization

Adaptive Data-Free Quantization

引用

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Biao Qian Yang Wang Richang Hong Meng Wang Key Laboratory of Knowledge Engineering with Big Data Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology China

data-free quantization (DFQ) recovers the performance of quantized network (Q) without the original data, but generates the fake sample via a generator (G) by learning from full-precision network (P), which, however, is totally independent of Q, overlooking the adaptability of the knowledge from generated samples, i.e., informative or not to the learning process of Q, resulting into the overflow of generalization error. Building on this, several critical questions — how to measure the sample adaptability to Q under varied bit-width scenarios? whether the largest adaptability is the best? how to generate the samples with adaptive adaptability to improve Q's generalization? To answer the above questions, in this paper, we propose an Adaptive data-Free Quantization (AdaDFQ) method, which revisits DFQ from a zero-sum game perspective upon the sample adaptability between two players — a generator and a quantized network. Following this viewpoint, we further define the disagreement and agreement samples to form two boundaries, where the margin between two boundaries is optimized to adaptively regulate the adaptability of generated samples to Q, so as to address the over-and-under fitting issues. Our AdaDFQ reveals: 1) the largest adaptability is NOT the best for sample generation to benefit Q's generalization; 2) the knowledge of the generated sample should not be informative to Q only, but also related to the category and distribution information of the training data for P. The theoretical and empirical analysis validate the advantages of AdaDFQ over the state-of-the-arts. Our code is available at https://***/hfutqian/AdaDFQ.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Co-training partial domain adaptation networks for industrial Fault Diagnosis

arXiv

引用

arXiv 2024年

作者： Chen, Gecheng Luo, Chengwen Li, Jianqiang Chen, Xinkai The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China The Department of Electronic Information Systems Shibaura Institute of Technology Saitama337-8570 Japan

The partial domain adaptation (PDA) challenge is a prevalent issue in industrial fault diagnosis. Current PDA approaches primarily rely on adversarial learning for domain adaptation and use reweighting strategies to exclude source samples deemed outliers. However, the transferability of features diminishes from general feature extraction layers to higher task-specific layers in adversarial learning-based adaptation modules, leading to significant negative transfer in PDA settings. We term this issue the adaptation-discrimination paradox (ADP). Furthermore, reweighting strategies often suffer from unreliable pseudo-labels, compromising their effectiveness. Drawing inspiration from traditional classification settings where such partial challenge is not a concern, we propose a novel PDA framework called Interactive Residual Domain Adaptation Networks (IRDAN), which introduces domain-wise models for each domain to provide a new perspective for the PDA challenge. Each domain-wise model is equipped with a residual domain adaptation (RDA) block to mitigate the ADP problem. Additionally, we introduce a confident information flow via an interactive learning strategy, training the modules of IRDAN sequentially to avoid cross-interference. We also establish a reliable stopping criterion for selecting the best-performing model, ensuring practical usability in real-world applications. Experiments have demonstrated the superior performance of the proposed IRDAN. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Comparative analysis of active contour random walker and watershed algorithms in segmentation of ovarian cancer 6

Comparative analysis of active contour random walker and wat...

引用

6th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2022

作者： Ruchitha, P.J. Sai, Richitha Y Kodipalli, Ashwini Martis, Roshan Joy Dasar, Santosh Ismail, Taha Global Academy of Technology Computer Science and Engineering Bangalore India Global Academy of Technology Artificial Intelligence and Data Science Bangalore India Sdm College of Medical Science Department of Radiologist Dharwad India

ISBN: (数字)9781665487160

ISBN: (纸本)9781665487160

In the recent era, Image processing has been one of the most commonly used domain in the field of medical science that includes different kinds of procedures namely extraction, image gaining, detection, surgical planning and presentation which indeed helps in giving effective treatment to the patient and using all those techniques, the disease could also be detected at a early stage. When it comes to the case of ovarian cancer, it could be treated successfully when detected at an early *** per the following statistics, it is very much important to identify the cancer in the ovaries at the starting stage. Today, there are many algorithms that are being implemented in order to detect the cancer i.e. by segmentation. Here are the three algorithms Random Walker, Active Contour and Watershed that are being implemented in order to segment the ovarian cancer. Finally, a comparative analysis is being performed to identify which gives a better result among the three algorithms. © 2022 IEEE.

关键词： Watersheds

来源：评论

学校读者我要写书评

暂无评论

An Efficient Automated Technique for Classification of Breast Cancer Using Deep Ensemble Model

引用

Computer Systems science & Engineering 2023年第7期46卷 897-911页

作者： Muhammad Zia Ur Rehman Jawad Ahmad Emad Sami Jaha Abdullah Marish Ali Mohammed A.Alzain Faisal Saeed Department of Electrical Engineering HITEC UniversityTaxila47080Pakistan School of Computing Edinburgh Napier UniversityEdinburghEH105DTUK Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia DAAI Research Group Department of Computing and Data ScienceSchool of Computing and Digital TechnologyBirmingham City University15 Bartholomew RowBirminghamB55JUUK

Breast cancer is one of the leading cancers among *** has the second-highest mortality rate in women after lung *** detection,especially in the early stages,can help increase survival ***,manual diagnosis of breast cancer is a tedious and time-consuming process,and the accuracy of detection is reliant on the quality of the images and the radiologist’s ***,computer-aided medical diagnosis has recently shown promising results,leading to the need to develop an efficient system that can aid radiologists in diagnosing breast cancer in its early *** research presented in this paper is focused on the multi-class classification of breast *** deep transfer learning approach has been utilized to train the deep learning models,and a pre-processing technique has been used to improve the quality of the ultrasound *** proposed technique utilizes two deep learning models,Mobile-NetV2 and DenseNet201,for the composition of the deep ensemble *** learning models are fine-tuned along with hyperparameter tuning to achieve better ***,entropy-based feature selection is *** cancer identification using the proposed classification approach was found to attain an accuracy of 97.04%,while the sensitivity and F1 score were 96.87%and 96.76%,*** performance of the proposed model is very effective and outperforms other state-of-the-art techniques presented in the literature.

关键词： Breast cancer image enhancement ensemble model transfer learning feature selection

来源：评论

学校读者我要写书评

暂无评论

Towards in X-Ray Induced Acoustic Imaging for Nasopharyngeal Carcinoma Radiotherapy with a Multidimensional Information Fusion Network 3

Towards in X-Ray Induced Acoustic Imaging for Nasopharyngeal...

引用

3rd International Conference on Computer Applications technology, CCAT 2024

作者： Jiang, Xin Chen, Zhi-Chao Sun, Zheng Liu, Ming-Zhe Chongqing University of Technology School of Artificial Intelligence Chongqing401135 China Chongqing University Cancer Hospital Department of Radiology Chongqing400030 China Chengdu University of Technology College of Nuclear Technology and Automation Engineering Chengdu610000 China Wenzhou University of Technology School of Data Science and Artificial Intelligence Wenzhou325000 China

ISBN: (纸本)9798331539795

X-ray-induced acoustic computed tomography (XACT) imaging is an emerging noninvasive imaging technique with wide applications in various clinical medicine fields. However, traditional XACT imaging methods often suffer from low imaging quality and increased noise artifacts, particularly notable under sparse sampling conditions. To overcome these challenges, we propose a hybrid supervised reconstruction method based on deep learning. Specifically, we extract and integrate information from XACT thermoacoustic signals and XACT images reconstructed by traditional methods to leverage complementary feature information from both modalities. To adapt to imaging under sparse sampling, we define multiple sparse conditions based on real-world imaging environments and validate them using a real radiotherapy dataset of head and neck cancer patients. The results demonstrate the adaptability of our method to sparse conditions and its capability to enhance XACT imaging quality under such circumstances. Additionally, we validate the feasibility of X-ray thermoacoustic imaging for 3D dose monitoring. © 2024 IEEE.

关键词： Radiotherapy

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：