检索结果-内蒙古大学图书馆

Applied Intelligence and Computing (AIC), IEEE World Conference on

作者： A. Ravisankar M. Ponniah Vikas Tripathi Kushagra Kulshreshtha Arun Pratap Srivastava Vikram Singh Department of Management Studies Erode Sengunthar Engineering College Erode PG and Research Department of Commerce G.T.N Arts College Dindigul Department of Computer Science & Engineering Graphic Era Deemed University Dehradun Uttarakhand Institute of Business Management GLA University Mathura Department of CSE Lloyd Institute of Engineering and Technology Greater Noida Quantum University Roorkee Uttarakhand India

Marketing strategies are being revolutionized by the development of user data and the expanding usability of Machine Learning (ML) as well as Big Data approaches. The wide variety of options that ML and Big Data applications provide in building and sustaining a competitive corporate edge are not fully understood by researchers and marketers. Based on a thorough analysis of academic and commercial literature, we offer a classification of ML and Big Data use cases in marketing in this article. In order to effectively employ ML and Big Data in marketing, we have discovered 11 recurrent use cases that are grouped into 4 homogenous families. These families are: fundamentals of the consumer, the consumer experience, decision-making, and financial impact. We go over the taxonomy's repeating patterns and offer a conceptual framework for understanding and extending it, emphasizing the practical ramifications for marketers and academics.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Flip Learning: Weakly Supervised Erase to Segment Nodules in Breast Ultrasound

arXiv

引用

arXiv 2025年

作者： Huang, Yuhao Chang, Ao Dou, Haoran Tao, Xing Zhou, Xinrui Cao, Yan Huang, Ruobing Frangi, Alejandro F. Bao, Lingyun Yang, Xin Ni, Dong National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Lab Shenzhen University Shenzhen China Marshall Laboratory of Biomedical Engineering Shenzhen University Shenzhen China School of Computing University of Leeds Leeds United Kingdom Shenzhen RayShape Medical Technology Co. Ltd Shenzhen China Division of Informatics Imaging and Data Science School of Health Sciences University of Manchester Manchester United Kingdom Department of Computer Science School of Engineering University of Manchester Manchester United Kingdom Department of Electrical Engineering Department of Cardiovascular Sciences KU Leuven Belgium Alan Turing Institute London United Kingdom NIHR Manchester Biomedical Research Centre Manchester Academic Health Science Centre Manchester United Kingdom Department of Ultrasound Affiliated Hangzhou First People’s Hospital School of Medicine Westlake University China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China

Accurate segmentation of nodules in both 2D breast ultrasound (BUS) and 3D automated breast ultrasound (ABUS) is crucial for clinical diagnosis and treatment planning. Therefore, developing an automated system for nodule segmentation can enhance user independence and expedite clinical analysis. Unlike fully-supervised learning, weakly-supervised segmentation (WSS) can streamline the laborious and intricate annotation process. However, current WSS methods face challenges in achieving precise nodule segmentation, as many of them depend on inaccurate activation maps or inefficient pseudo-mask generation algorithms. In this study, we introduce a novel multi-agent reinforcement learning-based WSS framework called Flip Learning, which relies solely on 2D/3D boxes for accurate segmentation. Specifically, multiple agents are employed to erase the target from the box to facilitate classification tag flipping, with the erased region serving as the predicted segmentation mask. The key contributions of this research are as follows: 1) Adoption of a superpixel/supervoxel-based approach to encode the standardized environment, capturing boundary priors and expediting the learning process. 2) Introduction of three meticulously designed rewards, comprising a classification score reward and two intensity distribution rewards, to steer the agents’ erasing process precisely, thereby avoiding both under- and over-segmentation. 3) Implementation of a progressive curriculum learning strategy to enable agents to interact with the environment in a progressively challenging manner, thereby enhancing learning efficiency. Extensively validated on the large in-house BUS and ABUS datasets, our Flip Learning method outperforms state-of-the-art WSS methods and foundation models, and achieves comparable performance as fully-supervised learning algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Deep and handcrafted feature supported diabetic retinopathy detection: A study

引用

Procedia computer Science 2023年 218卷 2675-2683页

作者： Seifedine Kadry Rubén González Crespo Enrique Herrera-Viedma Sujatha Krishnamoorthy Venkatesan Rajinikanth Faculty of Applied Computing and Technology Noroff University College Kristiansand 94612 Norway Computer Science Department School of Engineering and Technology Universidad Internacional de La Rioja 26006 Andalusian Spain Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Electronics and Instrumentation Engineering St. Joseph's College of Engineering OMR Chennai 600119 Tamil Nadu India

The eye is the prime sensory organ in physiology, and the abnormality in the eye severely influences the vision system. Therefore, eye irregularity is commonly assessed using imaging schemes, and Fundus Retinal Image (FRI) supported eye screening is one of the ophthalmological practices. This work proposed a Deep-Learning Procedure (DLP) to recognize Diabetic Retinopathy (DR) in FI. The proposed work presents the experimental work with different DLP methods found in the literature. This work is executed with two modes; (i) DR detection using conventional deep-features and (ii) DR discovery using deep ensemble features. To demonstrate this work, 1800 fundus images (900 regular and 900 DR class) are considered for the assessment, and the advantage of proposed plan is confirmed using various performance metrics. The experimental outcome of this study confirms that the AlexNet-based detection provides a better detection (>96%), and the deep ensemble features of AlexNet, VGG16, and ResNet18 provide a detection accuracy of >98% on the chosen FRI database.

关键词： Eye abnormality Diabetic retinopathy Fundus imaging Deep-Learning Classification

来源：评论

学校读者我要写书评

暂无评论

Automatic detection of lung nodule in CT scan slices using CNN segmentation schemes: A study

引用

Procedia computer Science 2023年 218卷 2786-2794页

作者： Seifedine Kadry Enrique Herrera-Viedma Rubén González Crespo Sujatha Krishnamoorthy Venkatesan Rajinikanth Faculty of Applied Computing and Technology Noroff University College Kristiansand 94612 Norway Research Institute in Data Science and Computational Intelligence University of Granada Granada Andalusian Spain Computer Science Department School of Engineering and Technology Universidad Internacional de La Rioja 26006 Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Electronics and Instrumentation Engineering St. Joseph's College of Engineering OMR Chennai 600119 Tamil Nadu India

The lung is one of the prime respiratory organs in human physiology, and its abnormality will severely disrupt the respiratory system. Lung Nodule (LN) is one of the abnormalities, and early screening and treatment are necessary to reduce its harshness. The proposed work aims to implement the Convolutional-Neural-Network (CNN) segmentation methodology to extract the LN in various lung CT slices, such as axial, coronal, and sagittal planes. This work consists of the following phases; (i) Image collection and pre-processing, (ii) Ground-truth generation, (iii) CNN-supported segmentation, and (iv) Performance evaluation and validation. In this work, the merit of pre-trained CNN segmentation schemes is verified using (i) One-fold training and (ii) Two-fold training methods. The test images for this study are collected from The Cancer Imaging Archive (TCIA) database. The experimental investigation is executed using Python®, and the outcome of this study confirms that the VGG-SegNet helps to get better values of Jaccard (>88%), Dice (>93%), and Accuracy (>96%) compared to other CNN methods.

关键词： Lung nodule CT scan CNN segmentation VGG-SegNet Accuracy Detection

来源：评论

学校读者我要写书评

暂无评论

Critical subgraphs of Schrijver graphs for the fractional chromatic number

arXiv

引用

arXiv 2022年

作者： Gujgiczer, Anna Simonyi, Gábor Department of Computer Science and Information Theory Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics MTA-BME Lendület Arithmetic Combinatorics Research Group ELKH Budapest Hungary Alfréd Rényi Institute of Mathematics Budapest Hungary

Schrijver graphs are vertex-color-critical subgraphs of Kneser graphs having the same chromatic number. They also share the value of their fractional chromatic number but Schrijver graphs are not critical for that. Here we present an induced subgraph of every Schrijver graph that is vertex-critical with respect to the fractional chromatic number. These subgraphs turn out to be isomorphic with certain circular complete graphs. We also characterize the critical edges within this *** Codes Primary: 05C15, Secondary: 05C60, 05C72 Copyright © 2022, The Authors. All rights reserved.

关键词： Graphic methods

来源：评论

学校读者我要写书评

暂无评论

Digitally tunable optical delay line based on thinfilm lithium niobate featuring high switching speed and low optical loss

引用

Photonics research 2022年第11期10卷 2575-2583页

作者： WEI KE YANMEI LIN MINGBO HE MENGYUE XU JIAXIANG ZHANG ZHONGJIN LIN SIYUAN YU XINLUN CAI State Key Laboratory of Optoelectronic Materials and Technologies School of Electronics and Information TechnologySun Yat-sen UniversityGuangzhou 510275China State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information TechnologyChinese Academy of SciencesShanghai 200092China Department of Electrical and Computer Engineering The University of British ColumbiaVancouverBritish Columbia V6T 1Z4Canada

A tunable optical delay line(ODL) featuring high switching speed and low optical loss is highly desirable in many fields. Here, based on the thin-film lithium niobate platform, we demonstrate a digitally tunable on-chip ODL that includes five Mach–Zehnder interferometer optical switches, four flip-chip photodetectors, and four delayline waveguides. The proposed optical switches can achieve a switching speed of 13 ns and an extinction ratio of34.9 dB. Using a modified Euler-bend-based spiral structure, the proposed delay-line waveguide can simultaneously achieve a small footprint and low optical propagation loss. The proposed ODL can provide a maximum delay time of 150 ps with a resolution of 10 ps and feature a maximum insertion loss of 3.4 dB.

关键词： tunable optical interferometer

来源：评论

学校读者我要写书评

暂无评论

Intra-Datacenter Load Balancing in a Federated Cloud with Throttled Algorithm

Intra-Datacenter Load Balancing in a Federated Cloud with Th...

引用

Science, engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Damola Gideon Akinola Emmanuel Adetiba Abdultaofeek Abayomi Uche Nnaji Surendra Thakur Sibusiso Moyo Department of Electrical and Information Engineering Covenant Applied Informatics and Communications African Center of Excellence Covenant University Ota Nigeria HRA Institute for Systems Science Durban University of Technology Durban South Africa Department of Information and Communication Technology Mangosuthu University of Technology Durban South Africa Dept. of Computer and Information Science Covenant Applied Informatics and Communications African Center of Excellence Covenant University Ota Nigeria KZN e-Skills CoLab Durban University of Technology Durban South Africa School for Data Science and Computational Thinking and Department of Mathematical Sciences Stellenbosch University Stellenbosch South Africa

ISBN: (数字)9798350358155

ISBN: (纸本)9798350358162

The emergence of cloud computing technology has motivated a very high number of users in different organizations to access its services in running and delivering their various operations and services. However, this surge of cloud users has led to the problem of uneven load sharing among the cloud computing infrastructures. As a result, federated cloud infrastructure was initiated to provide more cloud computing resources to accommodate more cloud users’ requests and also to provide equal requests mapping with the available cloud resources. Nevertheless, the issue of unequal allocation of requests within the datacenter(s) that makes up the federated cloud environment still persists. This research work presents an intra-datacenter load balancing algorithm in a federated cloud environment named; the intra-balancer adapted throttled algorithm to evenly distribute requests in each datacenter that formulate the federated cloud infrastructures. The simulation of the intra-balancer was carried out in CloudAnalyst, the results of the implementation showed that the intra-balancer outperformed the Round-Robin and ESCE with 98.93ms and 2.26ms for the response and processing time respectively. The results assert that the intra-balancer algorithm provides a better load balancing solution in a federated cloud environment.

关键词： computers Cloud computing Organizations Load management Resource management Time factors Task analysis

来源：评论

学校读者我要写书评

暂无评论

Electrical Fault Detection for Industry 4.0 using Fusion deep Learning Algorithm 12

Electrical Fault Detection for Industry 4.0 using Fusion dee...

引用

12th International Conference and Exposition on Electrical and Power engineering, EPE 2022

作者： Rajawat, Anand Singh Goyal, S.B. Bedi, Pradeep Mihaltan, Traian Candin Neagu, Bogdan-Constantin Raboaca, Maria Simona Verma, Chaman Sandip University School of Computer Science and Engineering Nashik India City University Faculty of Information Technology Petaling Jaya Malaysia Galgotais University Greater Noida201302 India Technical University of Cluj-Napoca Faculty of Building Services Cluj-Napoca Romania 'Gheorghe Asachi' Technical University of Iasi Department of Power Engineering Iasi700050 Romania National Research and Development Institute for Cryogenics and Isotopic Technologies Icsi Energy Department Ramnicu Valcea240050 Romania University of Eötvös Loránd Faculty of Informatics Budapest1053 Hungary

ISBN: (数字)9781665489942

ISBN: (纸本)9781665489942

In this paper, problems found in electrical power transmission lines are looked into and put into groups with the help of artificial neural networks. An idea is floating around for a method that uses the three-phase currents and the voltage at one end as inputs. In this process, both the feedforward neural network and the back propagation method are used. Both approaches have the ability to find errors in a system and put them into classes. So that it could be sure that the decision made by the neural network was the best one, a thorough analysis was done with a lot of different ways to set up the number of hidden layers. To design a smart model Electrical Fault Detection for Industry 4.0 using Fusion deep Learning Algorithm Simulations have shown that a neural network can reliably find and classify problems in transmission lines. © 2022 IEEE.

关键词： Electric power transmission

来源：评论

学校读者我要写书评

暂无评论

Robustness modularity in complex networks

引用

Physical Review E 2022年第5期105卷 054308-054308页

作者： Filipi N. Silva Aiiad Albeshri Vijey Thayananthan Wadee Alhalabi Santo Fortunato Indiana University Network Science Institute (IUNI) Bloomington Indiana 47408 USA Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah 21589 Kingdom of Saudi Arabia Luddy School of Informatics Computing and Engineering Indiana University Bloomington Indiana 47408 USA

A basic question in network community detection is how modular a given network is. This is usually addressed by evaluating the quality of partitions detected in the network. The Girvan-Newman (GN) modularity function is the standard way to make this assessment, but it has a number of drawbacks. Most importantly, it is not clearly interpretable, given that the measure can take relatively large values on partitions of random networks without communities. Here we propose a measure based on the concept of robustness: modularity is the probability to find trivial partitions when the structure of the network is randomly perturbed. This concept can be implemented for any clustering algorithm capable of telling when a group structure is absent. Tests on artificial and real graphs reveal that robustness modularity can be used to assess and compare the strength of the community structure of different networks. We also introduce two other quality functions: modularity difference, a suitably normalized version of the GN modularity, and information modularity, a measure of distance based on information compression. Both measures are strongly correlated with robustness modularity, but have lower time complexity, so they could be used on networks whose size makes the calculation of robustness modularity too costly.

关键词： Community structure Complex systems Robustness

来源：评论

学校读者我要写书评

暂无评论

Hybrid Optimization Method Using Simulated-Annealing-Based Ising Machine and Quantum Annealer

arXiv

引用

arXiv 2023年

作者： Kikuchi, Shuta Togawa, Nozomu Tanaka, Shu Department of Applied Physics and Physico-Informatics Keio University Kanagawa223-8522 Japan Department of Computer Science and Communications Engineering Waseda University Tokyo169-8555 Japan Keio University Tokyo108-8345 Japan Green Computing System Research Organization Waseda University Tokyo162-0042 Japan International Research Frontiers Initiative Tokyo Institute of Technology Tokyo108-0023 Japan

Ising machines have the potential to realize fast and highly accurate solvers for combinatorial optimization problems. They are classified based on their internal algorithms. Examples include simulated-annealing-based Ising machines (non-quantum-type Ising machines) and quantum-annealing-based Ising machines (quantum annealers). Herein we propose a hybrid optimization method, which utilizes the advantages of both types. In this hybrid optimization method, the preprocessing step is performed by solving the non-quantum-annealing Ising machine multiple times. Then sub-Ising models with a reduced size by spin fixing are solved using a quantum annealer. The performance of the hybrid optimization method is evaluated via simulations using Simulated Annealing (SA) as a non-quantum-type Ising machine and D-Wave Advantage as a quantum annealer. Additionally, we investigate the parameter dependence of the proposed hybrid optimization method. The hybrid optimization method outperforms the preprocessing SA and the quantum annealing machine alone in fully connected random Ising models. Copyright © 2023, The Authors. All rights reserved.

关键词： Simulated annealing

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：