检索结果-内蒙古大学图书馆

IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS)

作者： Kaviyaraj R P. Sathya P. Nandhini Vinoth Kumar P Sureshkumar Chelliah Ezhilmathi S Department of computational intelligence SRM institute of science and technology Kattankulathur India Department of Computer Science and Engineering Sri Shanmuga Collage of Engineering and Technology Sankari India Department of Artificial Intelligence and Data Science St. Joseph's Institute of Technology OMR Chennai

ISBN: (数字)9798350361186

ISBN: (纸本)9798350361193

Sign language, the vibrant tapestry of hand gestures and facial expressions, is the lifeblood of Deaf and hardof-hearing communities. For millions of signers, American Sign Language (ASL) runs deeper than communication, fundamental to identity, expression, and belonging. And yet an unshakeable communication gap leaves users of ASL frequently marooned away from the hearing world, kept from education, healthcare, or employment, or from basic, everyday transactions. By posing this new unsolved challenge to the power and promise of Artificial Intelligence (AI), this work leads the way towards closing that chasm by real-time recognition and translation of full ASL. Our approach employs a novel variant of Random Forest and utilizes cutting-edge video processing techniques to identify and understand the nuanced, often exquisitely delicate detail of ASL signing, at unprecedented levels of accuracy, and at speed. Another layer of innovation that characterizes our work is our integration of augmented reality (AR). By embedding AR along with our translator of artificial intelligence tech, we intend to completely change the way American Sign Language (ASL) is conveyed by directly engraining our already robust Random Forest model and advanced video processing techniques to project the ASL translation directly into your visual field in real time. The goal to demystify this complex and vivid language and, in doing so, to remove the communication barriers that persist between the Deaf community and the rest of the world, thus, fostering inclusion.

关键词： Sign language Computational modeling Signal processing algorithms Assistive technologies Predictive models Streaming media Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Research on a Technology of Mail Anti-virus Gateway for Industrial Internet

Research on a Technology of Mail Anti-virus Gateway for Indu...

引用

Image Processing and computer Applications (ICIPCA), IEEE International Conference on

作者： Yuhong Wu Yunshan Lv Ling Zhang School of Big Data & Computer Science Engineering Chongqing College of Mobile Communication Key Laboratory of Public Big Data Security Technology Chongqing China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China

With the rapid rise of the Industrial Internet, emails that convey a large amount of information require a strong network security environment. Aiming at the increasingly complex mail virus prevention and control work under the background of the industrial Internet, this paper designs and implements a mail anti-virus gateway system. First, design the overall process and structure of the mail virus processing system; then, design an extended mail receiving and sending protocol on the basis of the public framework, thereby ensuring the convenience of the system for use in different roles and environments. In the detection and filtering part of mail virus, an anti-virus engine containing multiple sub-function modules is designed to detect and filter viruses in mail text and attachments. Through the function test of 1000 kinds of virus mails, it is found that the mail anti-virus gateway system designed in this article can detect and filter most types of mail viruses. Compared with the virus detection on the client and server, the anti-virus gateway system has better performance And efficiency.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Spatial–temporal variations of NDVI and its response to climate in China from 2001 to 2020

引用

International Journal of Digital Earth 2022年第1期15卷 1463-1484页

作者： Yuanyuan Wei Shougang Sun Dong Liang Zhaohong Jia School of Internet Anhui UniversityHefeiPeople’s Republic of China National Engineering Research Center for Agro-Ecological Big Data Analysis & Application Anhui UniversityHefeiPeople’s Republic of China School of Computer Science and Technology Anhui UniversityHefeiPeople’s Republic of China

Vegetation plays an important role in global or regional environmental *** this study,the spatial–temporal variations of NDVI and its response to climate in China and its seven sub-regions were investigated based on MODIS NDVI data,ERA5-land precipitation(PRE)and temperature(TEM)data from 2001 to *** inter-annual growth rate of NDVI in China was 0.0021/yr in the past 20 *** inter-annual growth rates of NDVI in seven sub-regions had significant differences at regional or seasonal *** ratio of improved vegetation area to the total studied area reached about 70%.In summer,vegetation degradation was concentrated in East China and Southwest *** vegetation in Central China and South China improved more obviously in autumn than in the other *** vegetation of Northeast China had a remarkable degradation in autumn and winter,especially in *** influence degree of PRE(q=0.54,P<0.01)was greater than that of TEM(q=0.27,P<0.01)in the control of the spatial distribution of *** interaction influence degree q of PRE∩TEM was about 0.71 in the last 20 ***,the PRE and TEM played different roles in vegetation growth in seven sub-regions.

关键词： NDVI spatial–temporal variations climate change MK test Geodetector

来源：评论

学校读者我要写书评

暂无评论

Theoretical Insights for Diffusion Guidance: A Case Study for Gaussian Mixture Models

arXiv

引用

arXiv 2024年

作者： Wu, Yuchen Chen, Minshuo Li, Zihao Wang, Mengdi Wei, Yuting Department of Statistics and Data Science The Wharton School University of Pennsylvania United States Department of Electrical and Computer Engineering Princeton University United States

Diffusion models benefit from instillation of task-specific information into the score function to steer the sample generation towards desired properties. Such information is coined as guidance. For example, in text-to-image synthesis, text input is encoded as guidance to generate semantically aligned images. Proper guidance inputs are closely tied to the performance of diffusion models. A common observation is that strong guidance promotes a tight alignment to the task-specific information, while reducing the diversity of the generated samples. In this paper, we provide the first theoretical study towards understanding the influence of guidance on diffusion models in the context of Gaussian mixture models. Under mild conditions, we prove that incorporating diffusion guidance not only boosts classification confidence but also diminishes distribution diversity, leading to a reduction in the differential entropy of the output distribution. Our analysis covers the widely adopted sampling schemes including DDPM and DDIM, and leverages comparison inequalities for differential equations as well as the Fokker-Planck equation that characterizes the evolution of probability density function, which may be of independent theoretical interest. Copyright © 2024, The Authors. All rights reserved.

关键词： Diffusion

来源：评论

学校读者我要写书评

暂无评论

Age Detection in a Surveillance Video Using Deep Learning Technique

引用

SN computer science 2021年第4期2卷 249页

作者： Vasavi, S. Vineela, P. Raman, S. Venkat Department of Computer Science and Engineering VR Siddhartha Engineering College Vijayawada India Department of Space Advanced Data Research Institute Hyderabad India

The video streams that are collected from CCTV surveillance camera can be used in many applications such as crowd analysis, forensic, self-profile analysis, and social network user’s analysis. Soft biometrics such as age, gender, height, skin color can be used for human analysis. This requires object detection and feature analysis. Works reported for face recognition and age detection have poor performance with real-world profile images. It may be because of incomplete description of the human object. Also, they rely on traditional image processing algorithms that extract hand-crafted features. Deep learning workflow transforms the identified patterns into mathematical modeling that can be used for subsequent prediction. Residual networks can skip connections and can address vanishing gradient problem with improved accuracy. Wide ResNet 34-based system is proposed in this paper that automatically predicts age of human object in video images. Modified Wide ResNet is used for feature extraction that learns facial keypoints, image reconstruction using Simultaneous algebraic reconstruction technique for up sampling, 101 number of classes (101-way classification) ranging from 0 to 100. Proposed system accuracy is evaluated using mean absolute error and with Pearson correlation coefficient that finds correlation between actual age and predicted age. Experimental results proved that data augmented Wide ResNet out performs the existing age prediction methods with 5% increase in accuracy. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Age prediction data augmentation Feature extraction Image reconstruction Wide ResNet

来源：评论

学校读者我要写书评

暂无评论

Hybrid Approach of CNN & RNN for Breast Cancer Detection and Segmentation

Hybrid Approach of CNN & RNN for Breast Cancer Detection and...

引用

Smart Technologies and Systems for Next Generation Computing (ICSTSN), International Conference on

作者： P. Manju Bala S. Priyadharshini S. Usharani A. Balachandar Department of Artificial Intelligence and Data Science IFET College of Engineering (An Autonomous Institution) India Department of Computer Science and Engineering IFET College of Engineering (An Autonomous Institution) India Department of Artificial Intelligence and Machine Learning IFET College of Engineering (An Autonomous Institution) India

ISBN: (数字)9798350391565

ISBN: (纸本)9798350391572

Breast cancer is a serious worldwide health concern, and advanced diagnostic tools are needed for an accurate and timely identification of the illness. In order to classify breast cancer through ultrasound pictures, this method suggests a hybrid strategy that combines Convolutional Neural Networks (CNNs) with Recurrent Neural Networks (RNNs). CNN integration makes it possible to extract spatial data from ultrasound pictures and identify complex patterns that may be signs of cancer. Concurrently, RNNs are used to record temporal relationships between successive ultrasound frames, which enables the model to identify changing features of possible anomalies. By combining the advantages of both CNNs & RNNs, the suggested hybrid architecture improves the model's capacity to identify minute, dynamic patterns that are essential for precise diagnosis. In comparison to standalone CNN or RNN models, the hybrid model performs better in classification, as evidenced by experimental assessments carried out on a variety of ultrasound picture datasets. The findings highlight the hybrid approach's potential to improve breast cancer diagnostic skills and present a viable path for raising the precision and dependability of ultrasound-based tumour classification techniques.

关键词： Ultrasonic imaging Recurrent neural networks Accuracy Three-dimensional displays Breast cancer Spatial databases Planning

来源：评论

学校读者我要写书评

暂无评论

Customized CNN Model for Alzheimer's Disease Stages Classification and Comparison with Transfer Learning

Customized CNN Model for Alzheimer's Disease Stages Classifi...

引用

Recent Advances in Information Technology for Sustainable Development (ICRAIS), International Conference on

作者： Sindhu B Rishitha Prasad Ashwini Kodipalli Kumar Swamy S Trupthi Rao Department of Computer Science and Engineering Global Academy of Technology Bangalore Karnataka India Department of Artificial Intelligence and Data Science and Engineering Global Academy of Technology Bangalore Karnataka India

Alzheimer's disease, the predominant form of dementia, progressively worsens over time, initially causing mild memory loss and eventually severely hampering daily tasks and communication abilities. This degenerative condition affects regions of the brain integral to cognition, memory, and language. Due to its intricate nature, symptom experiences and progression can vary among individuals. This study proposes a predictive model to discern Alzheimer's stages. Leveraging “deep learning” techniques through convolutional neural networks (CNNs), the algorithm is trained on an extensive dataset of brain MRI scans. It learns to recognize pivotal features and correlate them with diverse disease phases. The Customized CNN model's accuracy of 94.37% surpasses alternative machine learning methods, including transfer learning. This precision empowers early prediction, significantly benefiting patient care and therapeutic strategies, thereby enhancing Alzheimer's disease management.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Measuring Diversity of Game Scenarios

arXiv

引用

arXiv 2024年

作者： Li, Yuchen Wang, Ziqi Zhang, Qingquan Yuan, Bo Liu, Jialin Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Tandon School of Engineering New York University United States School of Data Science Lingnan University Hong Kong

This survey comprehensively reviews the metrics for measuring the diversity of game scenarios, spotlighting the innovative use of procedural content generation and other fields as cornerstones for enriching player experiences through diverse game scenarios. By traversing a wide array of disciplines, from affective modeling and multi-agent systems to psychological studies, our research underscores the importance of diverse game scenarios in gameplay and education. Through a taxonomy of diversity metrics and evaluation methods, we aim to bridge the current gaps in literature and practice, offering insights into effective strategies for measuring and integrating diversity in game scenarios. Our analysis highlights the necessity for a unified taxonomy to aid developers and researchers in crafting more engaging and varied game worlds. This survey not only charts a path for future research in diverse game scenarios but also serves as a handbook for industry practitioners seeking to leverage diversity as a key component of game design and development. Copyright © 2024, The Authors. All rights reserved.

关键词： Taxonomies

来源：评论

学校读者我要写书评

暂无评论

Oral Cancer Detections and Classification Using Region Based Convolutional Neural Network

Oral Cancer Detections and Classification Using Region Based...

引用

Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： M Prema Kumar K Ashfaq Ahmed K Subash Anurag Aeron Department of ECE Shri Vishnu Engineering College for Women (A) Bhimavaram Andhra Pradesh India Department of Emerging Technologies in Computer Science G Pulla Reddy Engineering College(A) Kurnool Andhra Pradesh India Department of Data Science St. Joseph College (Autonomous) Trichy India Department of Computer Science and Engineering MIET Meerut Meerut India

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

Early detection dramatically increases the survival rate of oral cancer (OC). Artificial intelligence (AI) technology has garnered more attention in the field of diagnostic medicine in present periods. This study set out to assess the available data regarding AI's efficacy in OC diagnosis critically. Artificial intelligence diagnostic accuracy and capacity to detect early phases of OC were highlighted. In this project, performance indicators will be measured and oral cancer will be divided and classified using intelligent computing techniques. The role of oral cancer classification and detection to achieve a high recognition rate while leveraging the best theoretical components of oral cancer images, a newly established region-based Convolutional Neural Network (RCNN-COA) and the Chimp Optimization Algorithm were used to improve a Deep Learning Method. Then, using the recommended Chimp Optimization Algorithm (COA), a region-based convolutional neural network (R-CNN) classifier was trained using the acquired theoretical properties and the innovative image. A comparison of many deep learning and machine learning models' performances has been reported in a study. The findings imply that the deep learning model is capable of managing the problematic task of early oral malignant tumor detection.

关键词： Deep learning Machine learning algorithms Image recognition Classification algorithms Convolutional neural networks Task analysis Optimization

来源：评论

学校读者我要写书评

暂无评论

FAT: Tilted Federated Learning with Alternating Direction Method of Multipliers

FAT: Tilted Federated Learning with Alternating Direction Me...

引用

International Conference on computer Supported Cooperative Work in Design

作者： Bo Cui Zhen Yang College of Computer Science Inner Mongolia University Hohhot China Engineering Research Center of Ecological Big Data Ministry of Education Hohhot China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

While federated learning (FL) has made significant strides in addressing data privacy concerns, the challenges of heterogeneous data and unfair performance among participants remain substantial. Existing solutions confront challenges such as high computational costs, difficulty in balancing performance with fairness, and poor convergence in partially data heterogeneous environments. The alternating direction method of multipliers (ADMM) is a highly promising approach that effectively addresses issues related to data heterogeneity by imposing constraints on local client updates through dual variables. In this paper, we propose a novel FL framework, named FAT (tilted FL with ADMM), designed to address the issue of data heterogeneity while reducing bias and unfair treatment towards different clients, and it provides a better trade-off between accuracy and fairness. We conducted experiments on two real-world datasets, and the results demonstrate that, compared to existing methods, FAT significantly improves fairness while maintaining accuracy. Our experiments demonstrate that FAT significantly outperforms existing state-of-the-art methods in both accuracy and fairness, offering a superior trade-off between these crucial aspects.

关键词： Measurement data privacy Accuracy Federated learning Convex functions Fats Computational efficiency

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：