检索结果-内蒙古大学图书馆

Leveraging Multimodal Ensemble Fusion-Based Deep Learning for COVID-19 on Chest Radiographs

computer Systems science & engineering 2023年第10期47卷 873-889页

作者： Mohamed Yacin Sikkandar K.Hemalatha M.Subashree S.Srinivasan Seifedine Kadry Jungeun Kim Keejun Han Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia Department of Biomedical Engineering Bannari Amman Institute of TechnologySathyamangalam638401India Department of Computer Science and Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Institute of Biomedical Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesSaveetha UniversityChennai602105India Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea Division of Computer Engineering Hansung UniversitySeoul02876Korea

Recently,COVID-19 has posed a challenging threat to researchers,scientists,healthcare professionals,and administrations over the globe,from its diagnosis to its *** researchers are making persistent efforts to derive probable solutions formanaging the pandemic in their *** of the widespread and effective ways to detect COVID-19 is to utilize radiological images comprising X-rays and computed tomography(CT)*** the same time,the recent advances in machine learning(ML)and deep learning(DL)models show promising results in medical ***,the convolutional neural network(CNN)model can be applied to identifying abnormalities on chest *** the epidemic of COVID-19,much research is led on processing the data compared with DL techniques,particularly *** study develops an improved fruit fly optimization with a deep learning-enabled fusion(IFFO-DLEF)model for COVID-19 detection and *** major intention of the IFFO-DLEF model is to investigate the presence or absence of *** do so,the presented IFFODLEF model applies image pre-processing at the initial *** addition,the ensemble of three DL models such as DenseNet169,EfficientNet,and ResNet50,are used for feature ***,the IFFO algorithm with a multilayer perceptron(MLP)classification model is utilized to identify and classify *** parameter optimization of the MLP approach utilizing the IFFO technique helps in accomplishing enhanced classification *** experimental result analysis of the IFFO-DLEF model carried out on the CXR image database portrayed the better performance of the presented IFFO-DLEF model over recent approaches.

关键词： COVID-19 computer vision deep learning image classification fusion model

来源：评论

学校读者我要写书评

暂无评论

Self-Citations in Academic Excellence: Analysis of the Top 1% Highly Cited India-Affiliated Research Papers

arXiv

引用

arXiv 2025年

作者： Sharma, Kiran Khurana, Parul School of Engineering & Technology BML Munjal University Gurugram Haryana-122413 India Center for Advanced Data and Computational Science BML Munjal University Gurugram Haryana-122413 India School of Computer Applications Lovely Professional University Phagwara Punjab-144411 India

Citations demonstrate the credibility, impact, and connection of a paper with the academic community. Self-citations support research continuity, but, if excessive, may inflate metrics and raise bias concerns. The aim of the study is to examine the role of self-citations towards the research impact of India. To study this, 3.58 million papers affiliated with India from 1947 to 2024 in the Scopus database were downloaded, and 2.96 million were filtered according to document type and publication year up to 2023. Further filtering based on high citation counts identified the top 1% of highly cited papers, totaling 29,556. The results indicate that the impact of Indian research, measured by highly cited papers, has grown exponentially since 2000, reaching a peak during the 2011–2020 decade. Among the citations received by these 29,556 papers, 6% are self-citations. Papers with a high proportion of self-citations (>90%) are predominantly from recent decades and are associated with smaller team sizes. The findings also reveal that smaller teams are primarily domestic, whereas larger teams are more likely to involve international collaborations. Domestic collaborations dominate smaller team sizes in terms of both self-citations and publications, whereas international collaborations gain prominence as team sizes increase. The results indicate that while domestic collaborations produce a higher number of highly cited papers, international collaborations are more likely to generate self-citations. The top international collaborators in highly cited papers are the USA, followed by UK, and Germany. © 2025, CC BY.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Optimizing Healthcare Delivery through CloudBased Clinical Decision Support Systems

Optimizing Healthcare Delivery through CloudBased Clinical D...

引用

Emerging Trends in Industry 4.0 (ETI 4.0)

作者： U. Samson Ebenezar Jenila G. Vennila T. Suresh Balakrishnan Prabhakar Krishnan Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Computer Science and Engineering CARE College of Engineering Trichy Tamil Nadu Department of AI & ML School of Computing Mohan Babu University Tirupati Andhra Pradesh Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Center for Cyber security Systems and Networks Amrita Vishwa Vidyapeetham Amritapuri-campus India

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

The experiment aims at evaluating how four machine learning algorithms work: Decision Tree, Random Forest, SVM, and Neural Network, in a task, of binary classification. Models were evaluated using comprehensive metrics such as accuracy, precision, recall, F-score and AUC that revealed a good diagnostic performance. Models selected for the experiment and parameters tested on real datasets show steady changes in performance. The Neural Network model remained on top, achieving accuracy, precision, recall, F-score and AUC-ROC of 91.8%, 92.1%, 91.3%, 91.7% and 0.93 respectively. Random Forest displayed a competitive accordance along with the accuracy of 88.6%, precision of 89.2%, recall of 88.7%, and the F-score of 89.0%, and the AUC of 0.91. From the results, Support Vector Machine and Decision Tree models posted lower accuracy, recall, and fscores compared to the Neural Network and Random Forest models in most cases. These reveal the need for the improvement of the suitable machine learning model that comes from specific task needs as well as the consideration of each one of factors including accuracy, interpretability, and computational efficiency. Extensive and development of the model may result in more comprehensive understanding of how the model leads classifying of real-world applications such as medical diagnoses, fraud detection and target marketing.

关键词： Support vector machines Measurement Accuracy Machine learning algorithms Computational modeling Neural networks Medical services

来源：评论

学校读者我要写书评

暂无评论

MP-DPCC: A Motion Proxy-Based Dynamic Point Cloud Compression Framework

MP-DPCC: A Motion Proxy-Based Dynamic Point Cloud Compressio...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： ZhaoYi Jiang Dong Han Cao Song FangZhe Nan Bailin Yang School of Computer Science and Technolgy Zhejiang Gongshang University School of Statistics and Mathematics Zhejiang Gongshang University Economic Forecasting and Policy Simulation Laboratory Zhejiang Gongshang University Collaborative Innovation Center of Statistical Data Engineering Technology & Application Zhejiang Gongshang University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The increasing data volume and the demand for real-time transmission highlight the necessity for efficient compression of dynamic point cloud data. Existing methods primarily focus on reducing inter-frame redundancy by calculating per-point motion information, overlooking the computational and storage costs involved. In this paper, we propose a novel motion proxy-based dynamic point cloud compression framework to enhance the efficiency and accuracy of motion information utilization. Specifically, we introduce a feature proxy module to adaptively locate proxy points, which represent the overall motion through the motion of proxy points. Additionally, a motion enhancement module is employed to refine motion details and prevent local information loss caused by dense motion trajectories. Extensive experiments demonstrate the superiority of our approach. Compared with baseline methods, it achieves an average BD-rate improvement of 12.07% (D1) and 11.90% (D2).

关键词： Point cloud compression Costs Accuracy Dynamics Termination of employment Signal processing Real-time systems Acoustics Trajectory Speech processing

来源：评论

学校读者我要写书评

暂无评论

A Semi-Supervised Learning Framework with Cross-Magnification Attention for Glioma Mitosis Classification

A Semi-Supervised Learning Framework with Cross-Magnificatio...

引用

IEEE International Symposium on Biomedical Imaging

作者： Manh Duong Nguyen Nguyen Dang Huy Pham Phi Le Nguyen Minh N. Do Institute for AI Innovation and Socital Impact (AI4LIFE) Hanoi University of Science and Technology Hanoi Vietnam Coordinated Science Laboratory University of Illinois at Urbana-Champaign Hanoi - Amsterdam High School for the Gifted Vietnam Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign VinUni-Illinois Smart Health Center VinUniversity

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Mitotic figure counting plays a critical role in glioma grading and prognostication, yet manual counting remains time-consuming and subject to variability. The Glioma-MDC 2025 Challenge, hosted at ISBI 2025, aims to address this challenge by advancing automated solutions. In response, we present SEMA, a semi-supervised learning framework that incorporates a cross-magnification attention mechanism. SEMA mimics the multi-magnification workflow used by pathologists, employing attention to model interactions between cells and neighboring regions, effectively replicating how pathologists analyze whole slide images. Additionally, SEMA leverages the abundance of unlabeled cells in raw tissue images from competition datasets through a semi-supervised learning approach, progressively enhancing its robustness and performance. Extensive experiments demonstrate that SEMA outperforms other baselines by up to 40% in FI score, with each component contributing significantly to its success. Notably, SEMA achieves a perfect F1 score, securing top performance on the public leaderboard.

关键词： Analytical models Attention mechanisms Foundation models MIMICs Manuals Semisupervised learning Robustness Cancer Biomedical imaging

来源：评论

学校读者我要写书评

暂无评论

Generating Adversarial Samples For Training Wake-up Word Detection Systems Against Confusing Words

arXiv

引用

arXiv 2022年

作者： Wang, Haoxu Jia, Yan Zhao, Zeqing Wang, Xuyang Wang, Junjie Li, Ming School of Computer Science Wuhan University Wuhan China Data Science Research Center Duke Kunshan University Kunshan China AI Lab of Lenovo Research Beijing China

Wake-up word detection models are widely used in real life, but suffer from severe performance degradation when encountering adversarial samples. In this paper we discuss the concept of confusing words in adversarial samples. Confusing words are commonly encountered, which are various kinds of words that sound similar to the predefined keywords. To enhance the wake word detection system's robustness against confusing words, we propose several methods to generate the adversarial confusing samples for simulating real confusing words scenarios in which we usually do not have any real confusing samples in the training set. The generated samples include concatenated audio, synthesized data, and partially masked keywords. Moreover, we use a domain embedding concatenated system to improve the performance. Experimental results show that the adversarial samples generated in our approach help improve the system's robustness in both the common scenario and the confusing words scenario. In addition, we release the confusing words testing database called HI-MIA-CW for future research. Copyright © 2022, The Authors. All rights reserved.

关键词： Wakes

来源：评论

学校读者我要写书评

暂无评论

A Lightweight and Robust Access Control Protocol for IoT-based e-Healthcare Network

引用

IEEE Transactions on Mobile Computing 2025年

作者： Ghaffar, Zahid Kuo, Wen-Chung Mahmood, Khalid Tariq, Tayyaba Shamshad, Salman Das, Ashok Kumar Alenazi, Mohammed J.F. National Yunlin University of Science and Technology Graduate School of Engineering Science and Technology Douliu64002 Taiwan National Yunlin University of Science and Technology Department of Computer Science and Information Engineering Douliu64002 Taiwan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Korea University Department of Computer Science and Engineering College of Informatics 145 Anam-ro Seongbuk-gu Seoul02841 Korea Republic of King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh Saudi Arabia

Internet of Things (IoT) devices are crucial components in e-healthcare networks. It enables remote patient health monitoring and facilitates seamless communication among medical sensors, wearable devices, and healthcare providers through public communication channels. Despite these advantages, the use of public communication among medical sensors in e-healthcare networks introduces critical challenges, such as vulnerability to impersonation, physical capture, and ephemeral secret leakage, particularly in resource-constrained environments. In recent years, various access control protocols have been developed to mitigate these risks. However, these protocols often fail to ensure robust security while incurring significant communication and computation overhead. To overcome these limitations, we propose a lightweight and robust access control protocol for IoT-based e-healthcare networks using chaotic maps. We propose a novel protocol that integrates a PUF-based mechanism to mitigate the challenges of physical tampering and cloning attacks in e-healthcare networks. It leverages the inherent uniqueness of PUF and enhances security through the high-entropy properties of chaotic maps. We analyze the proposed protocol informally, which confirms that it significantly bolsters efficiency and security. We also validate the security using the Random or Real (RoR) model. Moreover, we verify the security of the proposed protocol using Scyther. These analyses highlight that the proposed protocol offers robust resistance to numerous attacks, such as impersonation, physical capture, and ephemeral secret leakage. Moreover, we also compare it with existing and relevant protocols. The comparative analysis showcases its superior performance. Notably, the proposed authentication protocol significantly reduces 46.84% computational overhead and decreases 31.30% communication overhead, underscoring its enhanced performance and resource efficiency. © 2002-2012 IEEE.

关键词： Authentication

来源：评论

学校读者我要写书评

暂无评论

Prediction Analysis for Diabetic Disease Using Machine Learning Model

Prediction Analysis for Diabetic Disease Using Machine Learn...

引用

Information Technology (OCIT), OITS International Conference on

作者： N. Senthamarai Jafar Ali Ibrahim Syed Masood M. Sathya N. S. Kalyan Chakravarthy Bharati Rathore M Mohamed Kalith Oli Department of Network and Communications SRM Institute of Science and Technology Chennai India Department of IoT School of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of IT Nadar Saraswathi College of Engineering and Technology Theni India Center for Data Science AI&ML QIS College of Engineering and Technology Ongole India Faculty of Business and Creative Industries University of South Wales Pontypridd UK School of Bio Sciences and Technology Vellore Institute of Technology Vellore India

ISBN: (数字)9798331510404

ISBN: (纸本)9798331510411

The Diabetes Disease Prediction System is based on predictive modeling, which forecasts the user's disease based on the information that the user inputs to the system, including the number of pregnancies, age, weight, blood pressure, insulin, glucose, and other factors contributing to diabetes. The system evaluates the data supplied by the user as input and outputs the likelihood of the prediction. Nowadays, better production results are key for machine learning activities. Research has been conducted on a dataset from the Kaggle data set where single machine learning algorithms have been applied, and recorded results have been compared with results of multialgorithms of machine learning using neural networks. Some major classification algorithms, Random Forest, SVM, and Hybrid model of SVM and Random Forest, have been broadly utilized to anticipate the disease, where different accuracies were obtained. data preprocessing and feature selection were performed before building the models to increase the model's accuracy. Evaluation of the models in the proposed paper is based on the model's accuracy. The hybrid model performed best with 83% accuracy.

关键词： Support vector machines Accuracy Machine learning algorithms Linear regression Predictive models Prediction algorithms Diabetes Classification algorithms Random forests Diseases

来源：评论

学校读者我要写书评

暂无评论

GeckoDAG: Towards a Lightweight DAG-Based Blockchain via Reducing data Redundancy

GeckoDAG: Towards a Lightweight DAG-Based Blockchain via Red...

引用

International Conference on Distributed Computing Systems

作者： Xiaohai Dai Yifan Zhou Jiang Xiao Feng Cheng Xia Xie Hai Jin Bo Li National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China School of Computer Science and Technology Hainan University China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

To overcome the scaling and performance limitations, the Directed Acyclic Graph (DAG) is utilized as the underlying storage model of blockchain systems, which enables concurrent transaction processing and confirmation. However, accompanied by high performance, DAG-based blockchains still suffer from the severe challenge of constrained storage scalability, i.e., expensive storage overhead. Based on an in-depth analysis of the data, we discover that the root cause of storage overhead stems from the considerable data redundancy in the DAG-based blockchains. In this paper, we propose GeckoDAG, a lightweight DAG-based blockchain, whose design consists of two steps. First, we abstract a storage model named Basic from the existing DAG-based blockchain systems, which offers both high performance and security. On top of Basic, we then devise GeckoDAG, which merges previous transactions into Transaction Union (TU) and reduces the data redundancy in TU, thus lowering the storage overhead. To evaluate our design, we implement a prototype of GeckoDAG and conduct various experiments on it. The experimental results demonstrate that GeckoDAG can offer storage scalability while maintaining the security and efficiency of DAG-based blockchains.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Harnessing Outer Space for Improved Electrocaloric Cooling

SSRN

引用

SSRN 2024年

作者： Seo, Dong Hyun Kim, Hyung Rae Yun, Joo Ho Kim, Do Hyeon Heo, Se-Yeon Ma, Rujun Lee, Gil Ju Song, Young Min School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Gwangju61005 Korea Republic of School of Materials Science and Engineering National Institute for Advanced Materials Smart Sensing Interdisciplinary Science Center Nankai University Tianjin300350 China Department of Electronics Engineering Pusan National University Busan46241 Korea Republic of AI Graduate School Gwangju Institute of Science and Technology Gwangju61005 Korea Republic of

Solid-state refrigeration, exploiting the electrocaloric (EC) effect, has sparked substantial interest due to its minimal energy consumption and zero-carbon emissions. However, one challenge to solid-state refrigeration technology resides in the limitation of cooling performance by the adiabatic temperature change (ΔT) inherent to caloric materials. To fully replace the traditional cooling technology, an augmentation of the innate heat dissipation capability beyond the intrinsic properties of the material is essential. In this study, we pioneer a method to transcend the native heat dissipation threshold of EC, by implementing a novel radiative heat sink/source-integrated electrocaloric (R-iEC) thermal regulation system. This system ingeniously leverages the outer cold space as a heat sink without energy. Moreover, a non-metallic thermally conductive radiative cooler (TCRC) is implemented with both a high thermal conductivity of 1.1 W/mK and exceptional spectral features (i.e., solar reflectivity ~96% and emissivity ~95% in atmospheric window region). The R-iEC system fortified with the TCRC demonstrates superior heat dissipation relative to conventional EC and radiative cooling, with an extra cooling heat flux of 87 W/m2. This improvement yields a temperature decrease of the heat source by 7.3 K relative to ambient temperature, showcasing the potential for a novel and zero-carbon emission cooling system. © 2024, The Authors. All rights reserved.

关键词： Heat flux

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：