检索结果-内蒙古大学图书馆

International Conference on data engineering

作者： Wensheng Luo Zhuo Tang Yixiang Fang Chenhao Ma Xu Zhou College of Computer Science and Electronic Engineering Hunan University China School of Data Science The Chinese University of Hong Kong Shenzhen China

As a fundamental problem in graph data mining, Densest Subgraph Discovery (DSD) aims to find the subgraph with the highest density from a graph. It has been studied for several decades and found a large number of real-world applications, such as network community detection, regulatory motif discovery in DNA, graph index construction, and fake follower detection. Although there are many existing DSD algorithms, they are often not scalable or efficient to process large-scale graphs, since most of them are serial algorithms and can only leverage the computing resource of a single CPU core. To tackle these issues, in this paper we propose efficient parallel algorithms for solving the DSD problems on both undirected and directed graphs at scale. Our main idea is to use the k-cores (a kind of dense subgraph) to approximate the densest subgraph in the undirected graphs, and then propose efficient parallel algorithms for computing the cores by optimizing the iterative process and also reducing the number of iterations. We further extend this idea for directed graphs by introducing a novel concept, named w-induced subgraph, to avoid unnecessary enumerations of x or y when searching [x,y]-cores (a kind of directed dense subgraph to approximate the densest). To verify the scalability and efficiency of the proposed algorithms, we have conducted extensive experiments on 12 large real-world graphs, and four of them are billion-scale. The experimental results show that our proposed algorithms outperform the state-of-the-art algorithms on both undirected and directed graphs, in terms of scalability and efficiency.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Skin Lesion Classification For Cancer Detection Using Convolutional Neural Network

Skin Lesion Classification For Cancer Detection Using Convol...

引用

Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering (RMKMATE), International Conference on

作者： M. A. Mukunthan Devaki Sai Sanuth Mohan Manikanta. V Dheeraj. K Dept. Of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Dept. Of Computer Science and Engineering With Artificial Intelligence and Machine Learning Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Dept. Of Computer Science and Engineering With Artificial Intelligence and Data Science Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798331598488

ISBN: (纸本)9798331598495

Skin cancer is a major global health issue with millions of annual cases diagnosed. In 2023, nearly 100,000 new cases of melanoma, which is the most deadly, are estimated in the United States. For improved survival rates and appropriate treatment, early detection is critical. Conversely, conventional diagnostic techniques are long-drawn-out and at times variabe which often triggers to diagnostic-introducing delays. We propose for the first vanishing point, to enhance their VII. A CNN model is trained on a wide dataset of dermoscopic images to classify a skin lesion as cancerous or non-cancerous. Performs analysis is robust using measures of accuracy, sensitivity, specificity, and AUC The outcomes reveal that the CNN-based algorithm achieves a high level of precision exceeding previous techniques and allowing for a rapid and accurate diagnosis. This technique could become a gamechanger in skin cancer detection with early intervention and treatment and ultimately improved patient outcomes and decreased costs to the healthcare industry and society.

关键词： Training Accuracy Computational modeling Transfer learning Medical services Skin Robustness Lesions Convolutional neural networks Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Customized Billboard Advertisement through Age and Gender Identification

Customized Billboard Advertisement through Age and Gender Id...

引用

2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Hariharan, Shanmugasundaram Gogula, Sreenivasulu Kukreja, Vinay Sheeba, R. D. S Terese Perumal, V. Jose, P. Vardhaman College of Engineering Department of Artificial Intelligence and Data Science Hyderabad India Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India Chitkara University Centre for Research Impact & Outcome Rajpura India Dmi Engineering College Department of Electronics and Communication Engineering Chennai India Vardhaman College of Engineering Dept. of Electrical and Electronics Engineering Hyderabad India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Cse Chennai India

ISBN: (纸本)9798350375442

This work provides a novel and sustainable advertisement display system that uses trained models, such as CNN and SSD, to adapt to the age and gender preferences of bystanders. By integrating a webcam with the advertising screen, the system is able to identify the age and gender of passersby based on the faces that are detected and processed by trained models. Customers can personalize adverts to certain groups by accessing the classification results via a website, which is accessed and stored in real-time on a database server. The system has a motion sensor for energy economy and runs continuously, updating every two seconds. With 91.7% and 95.5% accuracy rates for age and gender classification, respectively, the technology guarantees accurate content delivery. It also has the capacity to show news and guidance in crowded places, thereby fostering societal awareness and knowledge. The work well analyzes the gender detection using conventional methods and reveled it significance. © 2024 IEEE.

关键词： advertisement display system age classification decision making deep learning gender recognition

来源：评论

学校读者我要写书评

暂无评论

Task Prioritization in Multiagent Environments: A Novel Approach Using Nash Q-Learning

引用

IEEE Transactions on Consumer Electronics 2025年

作者： Li, Kexin Hu, Yu Wang, Jielei Li, Lin Zhang, Shuyuan Luo, Guangchun Laboratory of Intelligent Collaborative Computing University of Electronic Science and Technology of China China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Chengdu611731 China College of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

—With the rapid proliferation of smartphones and various terminal devices, edge computing has gained increasing importance in overcoming computational limitations and enhancing service quality through task offloading. However, most existing approaches handle tasks on a first-come, first-served basis, neglecting task prioritization, which is critical in real-world scenarios where tasks have different levels of urgency and resource requirements. This paper addresses the challenge of effective task prioritization in multi-agent task offloading by proposing a Nash Q-learning-based strategy. Our methodology involves optimizing task allocation decisions using Nash Q-learning to integrate Nash equilibrium principles, thereby enhancing adaptability and dynamic decision-making capabilities within a multi-agent environment. The proposed algorithm is evaluated through extensive simulations, comparing total delay, resource utilization, and task success rates across various task offloading strategies. The results demonstrate that our Nash Q-learning approach outperforms conventional techniques by dramatically lowering overall latency, optimizing resource allocation, and maintaining high task success rates. This work demonstrates the efficacy of Nash Q-learning for dynamically setting priorities for tasks in multi-agent edge computing environments, providing a robust framework for effective resource management and collaborative task completion. And also highlight the potential of Nash Q-learning in advancing intelligent task management in edge computing, suggesting its applicability in real-world, resource-constrained environments. © 2025 IEEE. All rights reserved,

关键词： Computation offloading

来源：评论

学校读者我要写书评

暂无评论

COVID19 detection in chest x-ray using vision-transformer with different patch dimensions 2

COVID19 detection in chest x-ray using vision-transformer wi...

引用

2nd International Conference on Machine Learning and data engineering, ICMLDE 2023

作者： Kadry, Seifedine Abualigah, Laith González Crespo, Rubén Verdú, Elena Damasevicius, Robertas Singh, Vijendra Rajinikanth, Venkatesan Department of Applied Data Science Noroff University College Kristiansand Norway Ajman University Ajman346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Computer Science Department Al Al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia Higher School of Engineering and Technology Universidad Internacional de la Rioja Logroño26006 Spain Department of Applied Informatics Vytautas Magnus University Kaunas53361 Lithuania School of Computer Science University of Petroleum and Energy Studies Dehradun248007 India Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India

computerized disease detection systems (CDDs) have proven effective for automatic screening in recent years. Among the standard procedures in hospitals for faster and more accurate diagnosis is medical imaging-based disease screening. We aim to develop a CDD that detects COVID-19 using chest X-rays pre-trained vision transformers (PVTs). This scheme includes the following steps: (1) collecting images and resizing them, (2) implementing PVT for feature extraction, and (3) binary classifying the results and validating the proposed schemes. To prove the merit of the developed scheme, 4800 images (2400 normal and 2400 COVID-19) are analyzed. MLP classifiers verify the PVT performance using patch sizes of 6, 12, and 24. A patch size 24 results in 97.5% accuracy for the proposed CDD system. When patch sizes are increased to 12, accuracy increases to over 98%. For this specific task, smaller patch sizes are more effective. These high-accuracy results demonstrate the effectiveness of the developed scheme for detecting COVID-19 in chest X-rays. © 2024 Elsevier B.V.. All rights reserved.

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

CNN segmentation of skin melanoma in pre-processed dermoscopy images 2

CNN segmentation of skin melanoma in pre-processed dermoscop...

引用

2nd International Conference on Machine Learning and data engineering, ICMLDE 2023

作者： Kadry, Seifedine Verdú, Elena Damasevicius, Robertas Abualigah, Laith Singh, Vijendra Rajinikanth, Venkatesan Department of Applied Data Science Noroff University College Kristiansand Norway Ajman University Ajman346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Higher School of Engineering and Technology Universidad Internacional de la Rioja Logroño26006 Spain Department of Applied Informatics Vytautas Magnus University Kaunas53361 Lithuania Computer Science Department Al Al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia School of Computer Science University of Petroleum and Energy Studies Dehradun248007 India Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India

Using medical data to improve diagnosis accuracy has recently become common practice in hospitals. A modern computing environment has enabled real-time diagnosis of medical data using Convolutional Neural Networks (CNNs). To extract and evaluate skin melanoma recorded with digital dermatoscopy images (DDI), we developed a CNN segmentation framework. In this proposal, four phases are proposed: (i) DDI collection and resizing, (ii) DDI enhancement using pre-processing techniques, (iii) CNN segmentation for lesion extraction, (v) Comparing the extracted sections to the ground truth images, and (v) Verifying whether the framework is valid. Using DDI pre-processed with (i) Traditional procedures, (ii) Otsu's thresholding, (iii) Kapur's thresholding, and (iv) Fuzzy-Tsallis thresholding, this proposal examines the different CNN segmentation schemes presented in the literature. For mining skin lesions, the Moth-Flame Algorithm (MFA) combined with tri-level thresholding achieves an optimal threshold for the DDI. With Fuzzy-Tsallis thresholding images, the VGG-UNet performs better than the alternatives. This framework helps to achieve better values of Jaccard (88.47±2.13%), Dice (93.08±1.17%), and Accuracy (98.64±0.71%) on the chosen DDI database. © 2024 Elsevier B.V.. All rights reserved.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Assessing the Efficacy of ML Techniques for Forecasting Healthcare Consumer Readmission: A Comparative Analysis of Risk Factors and Healthcare Interventions

Assessing the Efficacy of ML Techniques for Forecasting Heal...

引用

Smart Technologies for Sustainable Development Goals (ICSTSDG), International Conference on

作者： Loganayagi S T. Suresh Balakrishnan V.R. Vimal S. Alagu Thangam Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Department of Artificial Intelligence and Data Science S. A. Engineering College Chennai India

ISBN: (数字)9798331516284

ISBN: (纸本)9798331516291

This study evaluates the efficacy of machine learning techniques for predicting patient readmissions, focusing on the comparative analysis of various algorithms in identifying risk factors and the potential for optimizing healthcare interventions. Healthcare professionals now prioritize preventing avoidable hospital readmissions because of the substantial effects it has on patient outcomes and healthcare expenditures. Machine learning is a viable solution to this problem because of its capacity to analyse enormous volumes of data and identify significant patterns. In order to determine how well machine learning models, including Random Forest, Support Vector Machine (SVM), Logistic Regression, and Neural Networks, predict readmission risks based on variables like age, comorbidities, length of hospital stay, and past treatment history, the study evaluates these models' accuracy and performance. data was collected from electronic health records (EHRs) encompassing a wide range of clinical, demographic, and social factors. The results demonstrated that machine learning algorithms could significantly improve the prediction of readmissions, with Random Forest outperforming other techniques from a precision and recall perspective. Moreover, the study highlights the potential of machine learning in recommending timely, personalized healthcare interventions, such as tailored discharge planning and patient monitoring, which could reduce the likelihood of readmission. The findings highlight the revolutionary potential of machine learning in enhancing predictive healthcare systems, improving patient outcomes, and optimizing resource utilization.

关键词： Support vector machines Machine learning algorithms Accuracy Hospitals Machine learning Predictive models data models Sustainable development Random forests Strain

来源：评论

学校读者我要写书评

暂无评论

A Case Study on Context-Aware Neural Machine Translation with Multi-Task Learning

arXiv

引用

arXiv 2024年

作者： Appicharla, Ramakrishna Gain, Baban Pal, Santanu Ekbal, Asif Bhattacharyya, Pushpak Department of Computer Science and Engineering Indian Institute of Technology Patna India Wipro AI Lab45 London United Kingdom School of AI and Data Science Indian Institute of Technology Jodhpur India Department of Computer Science and Engineering Indian Institute of Technology Bombay India

In document-level neural machine translation (DocNMT), multi-encoder approaches are common in encoding context and source sentences. Recent studies (Li et al., 2020) have shown that the context encoder generates noise and makes the model robust to the choice of context. This paper further investigates this observation by explicitly modelling context encoding through multi-task learning (MTL) to make the model sensitive to the choice of context. We conduct experiments on cascade MTL architecture, which consists of one encoder and two decoders. Generation of the source from the context is considered an auxiliary task, and generation of the target from the source is the main task. We experimented with German–English language pairs on News, TED, and Europarl corpora. Evaluation results show that the proposed MTL approach performs better than concatenation-based and multi-encoder DocNMT models in low-resource settings and is sensitive to the choice of context. However, we observe that the MTL models are failing to generate the source from the context. These observations align with the previous studies, and this might suggest that the available document-level parallel corpora are not context-aware, and a robust sentence-level model can outperform the context-aware models. © 2024, CC0.

关键词： Neural machine translation

来源：评论

学校读者我要写书评

暂无评论

SPECTRAL PROPERTIES OF THE LAPLACIAN OF TEMPORAL NETWORKS FOLLOWING A CONSTANT BLOCK JACOBI MODEL

arXiv

引用

arXiv 2023年

作者： Kuncheva, Zhana Kounchev, Ognyan Data Science and Engineering Optima Partners London United Kingdom Department of Mathematics and Computer Science Bulgarian Academy of Sciences Sofia Bulgaria

We study the behavior of the eigenvectors associated with the smallest eigenvalues of the Laplacian matrix of temporal networks. We consider the multilayer representation of temporal networks, i.e. a set of networks linked through ordinal interconnected layers. We analyze the Laplacian matrix, known as supra-Laplacian, constructed through the supra-adjacency matrix associated with the multilayer formulation of temporal networks, using a constant block Jacobi model which has closed-form solution. To do this, we assume that the inter-layer weights are perturbations of the Kronecker sum of the separate adjacency matrices forming the temporal network. Thus we investigate the properties of the eigenvectors associated with the smallest eigenvalues (close to zero) of the supra-Laplacian matrix. Using arguments of perturbation theory, we show that these eigenvectors can be approximated by linear combinations of the zero eigenvectors of the individual time layers. This finding is crucial in reconsidering and generalizing the role of the Fielder vector in supra-Laplacian matrices. © 2023, CC BY.

关键词： Eigenvalues and eigenfunctions

来源：评论

学校读者我要写书评

暂无评论

Pixel-Level Polyp Segmentation Network Based on Parallel Feature Enhancement and Attention Mechanism

SSRN

引用

SSRN 2024年

作者： Xu, Haiping Wang, Jie Li, Zuoyong Cheng, Xuesong Teng, Shenghua College of Computer and Data Science Minjiang University Fuzhou China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China Fujian Key Laboratory of Medical Big Data Engineering Fujian Provincial Hospital Fuzhou China

Polyp segmentation, a crucial tool in medical diagnostics, effectively locates and segments polyps, thereby aiding clinicians in enhancing both detection efficiency and accuracy. To address the persistent challenges of imprecise polyp localization and ambiguous boundary segmentation, we propose a pixel-level polyp segmentation network based on a parallel feature enhancement and attention mechanism (PFEA-Net). The proposed PFEA-Net follows an enconder-decoder architecture. Given that polyps vary greatly in size and shape, PFEA-Net first utilizes the HarDNet68 as the encoder to extract multi-level feature representations. Then, a parallel feature enhancement module is designed to enrich the feature map of each level by integrating semantic information derived from higher-level features. Meanwhile, a parallel partial decoder module is used to introduce global contextual information. Finally, the reverse attention module refines the boundary features. PFEA-Net effectively extracts high-level representations to enhance feature depiction, capturing both detailed and semantic information in the images. Rigorous experiments conducted on five publicly available polyp datasets using six evaluation metrics conclusively demonstrate that the proposed network exhibits superior robustness and achieves more precise segmentation. Compared to other state-of-the-art algorithms, PFEA-Net distinguishes itself with exceptional performance. © 2024, The Authors. All rights reserved.

关键词： Pixels

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：