检索结果-内蒙古大学图书馆

Hybrid Models for Breast Cancer Detection via Transfer Learning Technique

computers, Materials & Continua 2023年第2期74卷 3063-3083页

作者： Sukhendra Singh Sur Singh Rawat Manoj Gupta B.K.Tripathi Faisal Alanzi Arnab Majumdar Pattaraporn Khuwuthyakorn Orawit Thinnukool Department of Information Technology JSS Academy of Technical EducationNoidaIndia JSS Academy of Technical Education NoidaIndia Department of Electronics and Communication Engineering JECRC University JaipurRajasthanIndia Harcourt Butler Technological University Kanpur India Department of Electrical Engineering Prince Sattam Bin Abdulaziz UniversityCollege of EngineeringAl Kharj16278Saudi Arabia Faculty of Engineering Imperial College LondonLondonSW72AZUK College of Arts Media and TechnologyChiang Mai UniversityChiang Mai50200Thailand

Currently,breast cancer has been amajor cause of deaths in women worldwide and the World Health Organization(WHO)has confirmed *** severity of this disease can be minimized to the large extend,if it is diagnosed properly at an early stage of the ***,the proper treatment of a patient having cancer can be processed in better way,if it can be diagnosed properly as early as possible using the better ***,it has been currently observed that the deep neural networks have delivered remarkable performance for detecting cancer in histopathological images of breast *** address the above said issues,this paper presents a hybrid model using the transfer learning to study the histopathological images,which help in detection and rectification of the disease at a low *** dataset experiments were carried out to validate the suggested hybrid model in this *** experimental results show that the proposed model outperformed the baseline methods,with F-scores of 0.81 for DenseNet+Logistic Regression hybrid model,(F-score:0.73)for Visual Geometry Group(VGG)+Logistic Regression hybrid model,(F-score:0.74)for VGG+Random Forest,(F-score:0.79)for DenseNet+Random Forest,and(F-score:0.79)for VGG+Densenet+Logistic Regression hybrid model on the dataset of histopathological images.

关键词： Histopathological deep neural network machine learning breast cancer binary classification transfer learning

来源：评论

学校读者我要写书评

暂无评论

Protein-Ligand Interaction Prediction with Effective Method of Graph Attention Networks and Long Short-Term Memory 2

Protein-Ligand Interaction Prediction with Effective Method ...

引用

2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Deepa, N. AlFatlawy, Ramy Shashidhara, K.S. Revathi, R. Reka, M. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India Department of Computer Applications Sona College of Arts and Science Salem India

ISBN: (纸本)9798331504960

Nowadays, the drug discovery field of molecules in protein-ligand interactions developing effective therapeutics for identifying potential drug candidates with spatial features to predict binding affinity. However, the existing Deep Learning (DL) based prediction approaches failed to interact and predict the binding affinity of protein-ligand precisely for drug design with binding affinity. To overcome this problem, a Graph Attention Networks with Long Short-Term Memory (GATN-LSTM) method is proposed with spatiotemporal forecasting for interaction between protein and ligand in binding affinity prediction for drug design. The proposed GATN-LSTM method is employed on DUD-E dataset consists of enhanced the prediction by capturing relationship (interaction) between protein and ligand in molecules for drug discovery using PubChem for prediction. Then, the preprocessed data of protein-ligand interaction is extracted using (CNN-GNN) for better generalization of both local and global features for predicting drug discovery. Finally, GATN-LSTM is employed for interaction of molecules with binding affinity which enhanced the drug design and also improved the prediction results. Experimental results of the proposed GATN-LSTM method attained accuracy (0.9992), F1-score (0.9985) are higher than existing prediction approaches such as Convolutional Neural Network (CNN) and Mutual Information-based Feature Selection (MIFS). © 2024 IEEE.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Multi-View Graph Embedding Learning for Image Co-Segmentation and Co-Localization

引用

IEEE Transactions on Circuits and Systems for Video technology 2024年第6期34卷 4942-4956页

作者： Huang, Aiping Li, Lijian Zhang, Le Niu, Yuzhen Zhao, Tiesong Lin, Chia-Wen Fuzhou University Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou350108 China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou350108 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Fuzhou University Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering The Fujian Science and Technology Innovation Laboratory for Optoelectronic Information Fuzhou350108 China Institute of Communications Engineering National Tsing Hua University Department of Electrical Engineering Hsinchu30013 Taiwan

Image co-segmentation and co-localization exploit inter-image information to identify and extract foreground objects with a batch mode. However, they remain challenging when confronted with large object variations or complex backgrounds. This paper proposes a multi-view graph embedding (MV-Gem) learning scheme which integrates diversity, robustness and discernibility of object features to alleviate this phenomenon. To encourage the diversity, the deep co-information containing both low-layer general representations and high-layer semantic information is generated to form a multi-view feature pool for comprehensive co-object description. To enhance the robustness, a multi-view adaptive weighted learning is formulated to fuse the deep co-information for feature complementation. To ensure the discernibility, the graph embedding and sparse constraint are embedded into the fusion formulation for feature selection. The former aims to inherit important structures from multiple views, and the latter further selects important features to restrain irrelevant backgrounds. With these techniques, MV-Gem gradually recovers all co-objects through optimization iterations. Extensive experimental results on real-world datasets demonstrate that MV-Gem is capable of locating and delineating co-objects in an image group. © 1991-2012 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Intra-class patch swap for self-distillation

引用

Neurocomputing 2025年 645卷

作者： Choi, Hongjun Jeon, Eun Som Shukla, Ankita Turaga, Pavan Lawrence Livermore National Laboratory LivermoreCA94550 United States Department of Computer Science and Engineering Seoul National University of Science and Technology Seoul01811 Korea Republic of Department of Computer Science and Engineering University of Nevada RenoNV89557 United States Geometric Media Lab School of Arts Media and Engineering and School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85281 United States

Knowledge distillation (KD) is a valuable technique for compressing large deep learning models into smaller, edge-suitable networks. However, conventional KD frameworks rely on pre-trained high-capacity teacher networks, which introduce significant challenges such as increased memory/storage requirements, additional training costs, and ambiguity in selecting an appropriate teacher for a given student model. Although a teacher-free distillation (self-distillation) has emerged as a promising alternative, many existing approaches still rely on architectural modifications or complex training procedures, which limit their generality and efficiency. To address these limitations, we propose a novel framework based on teacher-free distillation that operates using a single student network without any auxiliary components, architectural modifications, or additional learnable parameters. Our approach is built on a simple yet highly effective augmentation, called intra-class patch swap augmentation. This augmentation simulates a teacher–student dynamic within a single model by generating pairs of intra-class samples with varying confidence levels, and then applying instance-to-instance distillation to align their predictive distributions. Our method is conceptually simple, model-agnostic, and easy to implement, requiring only a single augmentation function. Extensive experiments across image classification, semantic segmentation, and object detection show that our method consistently outperforms both existing self-distillation baselines and conventional teacher-based KD approaches. These results suggest that the success of self-distillation could hinge on the design of the augmentation itself. Our codes are available at https://***/hchoi71/Intra-class-Patch-Swap. © 2025 Elsevier B.V.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

Data-Driven Ground-Fault Location Method in Distribution Power System With Distributed Generation

arXiv

引用

arXiv 2024年

作者： Caporuscio, Mauro Dupuis, Antoine Löwe, Welf Department of Computer Science and Media Technology Linnaeus University Sweden Department of Electrical Engineering Uppsala University Sweden

The recent increase in renewable energy penetration at the distribution level introduces a multi-directional power flow that outdated traditional fault location techniques. To this extent, the development of new methods is needed to ensure fast and accurate fault localization and, hence, strengthen power system reliability. This paper proposes a data-driven ground fault location method for the power distribution system. An 11-bus 20 kV power system is modeled in Matlab/Simulink to simulate ground faults. The faults are generated at different locations and under various system operational states. Time-domain faulted three-phase voltages at the system substation are then analyzed with discrete wavelet transform. Statistical quantities of the processed data are eventually used to train an Artificial Neural Network (ANN) to find a mapping between computed voltage features and faults. Specifically, three ANNs allow the prediction of faulted phase, faulted branch, and fault distance from the system substation separately. According to the results, the method shows good potential, with a total relative error of 0,4% for fault distance prediction. The method is applied to datasets with unknown system states to test robustness. © 2024, CC BY.

关键词： Time domain analysis

来源：评论

学校读者我要写书评

暂无评论

New 5G Kaiser-Based Windowing to Reduce Out of Band Emission

引用

computers, Materials and Continua 2022年第2期71卷 2721-2738页

作者： Hammoodi, Ahmed Audah, Lukman Al-Jobouri, Laith Mohammed, Mazin Abed Aljumaily, Mustafa S. Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia Johor Batu Pahat86400 Malaysia School of Engineering Arts Science and Technology University of Suffolk IP4 1QJ United Kingdom College of Computer Science and Information Technology University of Anbar Anbar 31001 Iraq Electrical Engineering and Computer Science Department University of Tennessee KnoxvilleTN37901 United States

OFDM based waveforms are considered as the main part of the latest cellular communications standard (namely 5G). Many inherited problems from the OFDM-Based LTE are still under investigation. Getting rid of the out of band emissions is one of these problems. Ensuring low out of band emission (OOBE) is deemed as one of the most critical challenges to support development of future technologies such as 6G and beyond. Universal Filtered Multi Carrier (UFMC) has been considered as one of the candidate waveforms for the 5G communications due to its robustness against Inter Carrier Interference (ICI) and the Inter Symbol Interference (ISI). It is also a preferred option because it is the most appropriate for low latency scenarios. In this paper, a novel approach is proposed that makes use of modified Kaiser-Bessel filter-based pulse windowing instead of standard Dolph-Chebyshev filter for UFMC based waveform. The aim of proposing the new approach is to enable the reduction of spectral leakage into nearby sub-bands. A comprehensive study for the modified Kaiser-Bessel filters is performed and the results are presented in terms of several Key Performance Indicators (KPIs). Based on the results of the simulation, the UFMC Kaiser-Hankel window demonstrated lower sidebands and better power spectral density, when compared with the traditional Orthogonal frequency-division multiplexing (OFDM) and UFMC as well as the normal UFMC Kaiser window. In addition, the real test for the kaiser window with 5G waveform is lower OOBE than conventional 5G waveform (CP-OFDM and UFMC). The OOBE reduction of 31% of the Kaiser vs. the Dolph-Chebyshev filter, 68% of Kaiser Hankel over the Dolph-Chebyshev, and 20% of Kaiser Hankel over Kaiser filter have been reported in this paper. The Power Spectral Density has been improved accordingly. © 2022 Tech Science Press. All rights reserved.

关键词： Orthogonal frequency division multiplexing

来源：评论

学校读者我要写书评

暂无评论

Optimization method for a radar situation interface from error-cognition to information feature mapping

引用

Journal of Systems engineering and Electronics 2022年第4期33卷 924-937页

作者： WU Xiaoli WEI Wentao CALDWELL Sabrina XUE Chengqi WANG Linlin School of Design Art&Media Nanjing University of Science&TechnologyNanjing 210094China School of Mechanical Engineering Southeast UniversityNanjing 211189China College of Mechanical and Electrical Engineering Hohai UniversityChangzhou 211100China School of Computer Science Australian National UniversityCanberra 2600Australia

With the rapid development of digital and intelligent information systems, display of radar situation interface has become an important challenge in the field of human-computer interaction. We propose a method for the optimization of radar situation interface from error-cognition through the mapping of information characteristics. A mapping method of matrix description is adopted to analyze the association properties between error-cognition sets and design information sets. Based on the mapping relationship between the domain of error-cognition and the domain of design information, a cross-correlational analysis is carried out between error-cognition and design *** obtain the relationship matrix between the error-cognition of correlation between design information and the degree of importance among design information. Taking the task interface of a warfare navigation display as an example, error factors and the features of design information are extracted. Based on the results, we also propose an optimization design scheme for the radar situation interface.

关键词： radar situation interface error-cognition information feature mapping visual information display

来源：评论

学校读者我要写书评

暂无评论

Navigating the Nexus: A systematic review of the symbiotic relationship between the metaverse and gaming

引用

International Journal of Cognitive Computing in engineering 2024年第1期5卷 88-103页

作者： Mohammed, Sahar Yousif Aljanabi, Mohammed Gadekallu, Thippa Reddy Translation Department Faculty of Arts Anbar University Anbar31001 Iraq Imam Ja'afar Al-Sadiq University Baghdad Iraq Department of Computer College of Education Al-Iraqia University Baghdad 10011 Iraq Division of Research and development Lovely Professional University Phagwara India Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Centre of Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India

The advent of the metaverse has sparked profound interest in its integration within the gaming domain. Games, being intrinsic components of the metaverse, have attracted considerable attention, prompting inquiries into the transformative influence of the metaverse on the gaming industry and its communities within this immersive digital realm. This study aims to synthesize the literature by providing a comprehensive overview of recent studies on the complex interplay between video games and the metaverse, illuminating the benefits, difficulties, and uncharted territory of this field. Fifteen (15) relevant studies were reviewed in accordance with PRISMA criteria, supplemented with the LDA topic modeling to answer the developed research questions. The research showed that the metaverse has many impacts on the gaming industry;it serves as a platform for interactive games, a driver for collaborative gaming environments, a medium for immersive virtual reality (VR) experiences, and a source of useful information about the basic aspects and mechanisms that support the connected relationship between the metaverse and gaming. Furthermore, the evolution of these dimensions and processes across time was investigated. This systematic review makes a noteworthy addition to the ongoing scholarly discussion over the convergence of the metaverse and gaming. This statement highlights the significant capacity for transformation that arises from the integration of these elements, while also recognizing the intricate and demanding nature of this process. The ongoing transformation of the gaming industry and its communities by the metaverse prompts an investigation that lays the groundwork for future scholarly inquiry. This study aims to provide a detailed examination and enhanced comprehension of the intricate relationship between these two domains. © 2024 The Authors

关键词： Virtual reality

来源：评论

学校读者我要写书评

暂无评论

Tactile Displays Driven by Projected Light

arXiv

引用

arXiv 2024年

作者： Linnander, Max Goetz, Dustin Reardon, Gregory Kumar, Vijay Hawkes, Elliot Visell, Yon Department of Mechanical Engineering United States Department of Bioengineering United States Media Arts & Technology Program United States Department of Electrical and Computer Engineering University of California Santa Barbara United States

Tactile displays that lend tangible form to digital content could transform computing interactions. However, achieving the resolution, speed, and dynamic range needed for perceptual fidelity remains challenging. We present a tactile display that directly converts projected light into visible tactile patterns via a photomechanical surface populated with millimeter-scale optotactile pixels. The pixels transduce incident light into mechanical displacements through photostimulated thermal gas expansion, yielding millimeter scale displacements with response times of 2 to 100 milliseconds. Employing projected light for power transmission and addressing renders these displays highly scalable. We demonstrate optically driven displays with up to 1,511 addressable pixels – several times more pixels than any prior tactile display attaining comparable performance. Perceptual studies confirm that these displays can reproduce diverse spatiotemporal tactile patterns with high fidelity. This research establishes a foundation for practical, versatile high-resolution tactile displays driven by light. Copyright © 2024, The Authors. All rights reserved.

关键词： Light transmission

来源：评论

学校读者我要写书评

暂无评论

mediating The Marginal: A Quantitative Analysis of Curated LGBTQ+ Content on Instagram 25

Mediating The Marginal: A Quantitative Analysis of Curated L...

引用

2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

作者： Souza, Garrett Lutz, Nina Turner, Katlyn M Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA United States Department of Human Centered Design and Engineering University of Washington Seattle WA United States Media Lab Massachusetts Institute of Technology Cambridge MA United States

ISBN: (纸本)9798400713941

Control and curation of dominant visual culture - rendering who and what is visible - is central to identity formation, particularly for LGBTQ+ communities relying on digital spaces for safe self-expression. In this work, we analyze Instagram as a site of algorithmic visual curation, performing a quantitative analysis of algorithmically mediated image feeds delivered to a gay-coded user. Our persona account exclusively followed #gay and #instagay feeds, and engaged in content within these discursive spaces to seed algorithmic content promotion to a normative gay user. We present an analysis of skin tone presentations, emoji usage, and engagement metrics alongside analysis of generative outputs of dominant visual trends within the #gay search and Explore feeds. We observe content depicting darker-skinned individuals has higher engagement yet less algorithmic promotion relative to lighter skin tones, while hypermasculine and homonormative content is heavily promoted. These results suggest that, while marginalized positionalities have certainly been rendered more visible through social media platforms, this visibility is increasingly contingent on assimilation to normative ideals through algorithmically determined modes that are not necessarily consistent with user choices, preferences, or realities. © 2025 Copyright held by the owner/author(s).

关键词： LGBTQ+ Visual Content Recommendation Algorithms Social Networks

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：