检索结果-内蒙古大学图书馆

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Sirija, M. Dongre, Yashwant V. Singh, Akhilendra Pratap Haripriya, V. Aggarwal, Prateek Singh, Satendra Prince Shri Venkateshwara Padmavathy Engineering College Department of Information Technology Chennai127 India Vishwakarma Institute of Technology Department of Computer Engineering Pune India Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Department of Computer Science and Information Technology Karnataka Bangalore India Chitkara University Institute of Engineering and technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Vivekananda Global University Department of Mechanical Engineering Jaipur India

ISBN: (纸本)9798350370249

Encapsulating protection Payload (ESP) is a safety protocol used to provide authentication, integrity, and confidentiality in Wi-Fi networks. A latest research has explored the diverse vulnerabilities that exist in ESP, which can be exploited to gain get admission to confidential information. The principle awareness of the study become to perceive the weaknesses and strategies used to attack ESP implementations in a Wi-Fi network. the security of ESP become evaluated by using looking on the distinctive sorts of get right of entry to control techniques carried out, the energy of encryption algorithms used, and the trying out of the protocol's resiliency to positive attack scenarios. It was concluded that despite the fact that ESP is a powerful and cozy protocol, it nevertheless lacks in certain regions which need to be addressed if you want to make sure more safety in Wi-Fi networks. © 2024 IEEE.

关键词： Addressed Awareness Encapsulating Implementations Networks

来源：评论

学校读者我要写书评

暂无评论

Dynamic Multi-Layer Perceptron for Fetal Health Classification Using Cardiotocography Data

引用

computers, Materials & Continua 2024年第8期80卷 2301-2330页

作者： Uddagiri Sirisha Parvathaneni Naga Srinivasu Panguluri Padmavathi Seongki Kim Aruna Pavate Jana Shafi Muhammad Fazal Ijaz Department of Computer Science and Engineering Prasad V Potluri Siddhartha Institute of TechnologyVijayawada520007India Amrita School of Computing Amrita Vishwa VidyapeethamAmaravatiAndhra Pradesh522503India Department of Tele informatics Engineering Federal University of CearáFortaleza60455-970Brazil Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaramGunturAndhra Pradesh522302India Department of Computer Engineering Chosun UniversityGwangju61452Republic of Korea School of CSIT Symbiosis Skills and Professional UniversityPune412101India Department of Computer Engineering and Information College of Engineering inWadi AlddawasirPrince Sattam bin Abdulaziz UniversityWadi Alddawasir11991Saudi Arabia School of IT and Engineering Melbourne Institute of TechnologyMelbourne3000Australia

Fetal health care is vital in ensuring the health of pregnant women and the *** check-ups need to be taken by the mother to determine the status of the fetus’growth and identify any potential *** know the status of the fetus,doctors monitor blood reports,Ultrasounds,cardiotocography(CTG)data,***,in this research,we have considered CTG data,which provides information on heart rate and uterine contractions during *** researchers have proposed various methods for classifying the status of fetus *** processing of CTG data is time-consuming and ***,automated tools should be used to classify fetal *** study proposes a novel neural network-based architecture,the Dynamic Multi-Layer Perceptron model,evaluated from a single layer to several layers to classify fetal *** strategies were applied,including pre-processing data using techniques like Balancing,Scaling,Normalization hyperparameter tuning,batch normalization,early stopping,etc.,to enhance the model’s performance.A comparative analysis of the proposed method is done against the traditional machine learning models to showcase its accuracy(97%).An ablation study without any pre-processing techniques is also *** study easily provides valuable interpretations for healthcare professionals in the decision-making process.

关键词： Fetal health cardiotocography data deep learning dynamic multi-layer perceptron feature engineering

来源：评论

学校读者我要写书评

暂无评论

Domain Contrastive Learning for Multi-Site Alzheimer's Disease Classification 20

Domain Contrastive Learning for Multi-Site Alzheimer's Disea...

引用

20th International computer Conference on Wavelet Active Media technology and information Processing, ICCWAMTIP 2023

作者： Fiasam, Linda Delali Rao, Yunbo Sey, Collins Aggrey, Esther Stacy E. B. Kodjiku, Seth Larweh Browne, Judith Ayekai Danso, Juliana Mantebea Ukwuoma, Chiagoziem C. Gyarteng, Emmanuel S. A. School of Information and Software Engineering University of Electronic Science and Technology of China China School of Computer Science and Technology Zhejiang Gongshang University China School of Computer Science and Technology University of Electronic Science and Technology of China University China College of Nuclear Technology and Automation Engineering Chengdu University of Technology China

ISBN: (纸本)9798350318982

In the field of medical imaging, accurately classifying Alzheimer's Disease (AD) poses a substantial challenge, primarily due to the inherent variability in imaging data across different clinical sites. This paper introduces a novel approach, Domain Contrastive Learning (DCL), to address this challenge by enhancing the robustness and transferability of AD classification models using multi-site Magnetic Resonance Imaging (MRI) data. DCL leverages a unique contrastive learning technique within an autoencoder's latent space, enabling the extraction of domain-invariant features essential for accurate diagnosis. We rigorously evaluate the proposed network on brain images from three distinct imaging sites, employing a Domain Generalization (DG) training protocol to ensure robustness and generalizability across varying data sources. Our model is evaluated on three binary classification tasks: AD vs. Normal Control (NC), AD vs. Mild Cognitive Impairment (MCI), and MCI vs. NC. Results demonstrate that DCL outperforms existing methods, especially in AD vs. NC classification, achieving higher accuracy, sensitivity, and specificity with 0.51%, 1.54% and 1.68% increments respectively. This underscores DCL's potential as a robust and generalizable method for enhanced AD diagnosis, marking a significant step forward in medical imaging challenges in a multi-site context. © 2023 IEEE.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Compressed Video Enhancement with Spatio-Temporal Multi-Feature Extraction

Compressed Video Enhancement with Spatio-Temporal Multi-Feat...

引用

2023 China Automation Congress, CAC 2023

作者： Ru, Haihan Chen, Jing Chen, Kemi Zuo, YuTing Chen, Xia College of Information Science and Engineering Huaqiao University Xiamen China College of Computer Science and Technology Huaqiao University Xiamen China

ISBN: (纸本)9798350303759

Bandwidth limitations require transmitting the original video in a compressed format, leading to video artifacts and quality degradation. Therefore, compressed video enhancement is very important. In recent years, deep learning methods have gained prominence in compressed video enhancement due to their ability to efficiently restore lost details in specific frames by extracting features. information extraction can be influenced by video artifacts in preceding and subsequent frames, even when techniques like optical flow estimation or quality-based frame selection are employed. To better capture artifact removal details, this paper introduces a spatiotemporal multi-feature extraction method for more accurate video reconstruction. The experimental results show that the peak Signal-To-Noise Ratio (PSNR) of the video enhanced by the method proposed in this paper is about 0.06 dB higher(QP37) on average when compared to the video enhanced by some other methods (e.g., MFQE2.0, MFQE1.0, DS-CNN). © 2023 IEEE.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Visible Watermark Removal with Deep Learning technology 6

Visible Watermark Removal with Deep Learning Technology

引用

6th International Symposium on computer, Consumer and Control, IS3C 2023

作者： Lin, Chia-Chen Wang, Pei-Yu Lin, Yan-Heng Huang, Hsuan-Chao Saberikamposhti, Morteza National Chin-Yi University of Technology Dept.of Computer Science and Information Engineering Taichung Taiwan Universiti Kebangsaan Malaysia Dept. of Information Science & Technology Selangor Malaysia

ISBN: (纸本)9798350301953

Watermarking is a technique used to assert ownership over an image, and can be categorized into visible and invisible forms based on the detectability of the watermark. Visible watermarking is more user-friendly and intuitive than invisible methods since it allows individuals to identify image ownership with their own eyes rather than relying on machine-based watermark decoders. To enhance the visual quality of watermarked images and ensure the original images can be fully recovered after visible watermark authentication, a visible watermark removal approach using deep learning-based inpainting is proposed in this paper. Experimental results demonstrate that the watermarked images carrying the visible watermark and auxiliary information achieve peak signal-to-noise ratios (PSNRs) ranging from 41.89 dB to 43.17 dB and structural similarity indices (SSIMs) up to 0.97 to 0.98. Furthermore, our hybrid recovery operations enable the complete restoration of the original images, making them easily readable. © 2023 IEEE.

关键词： Watermarking

来源：评论

学校读者我要写书评

暂无评论

Pre-Impact and Impact Fall Detection Based on a Multimodal Sensor Using a Deep Residual Network

引用

Intelligent Automation & Soft Computing 2023年第6期36卷 3371-3385页

作者： Narit Hnoohom Sakorn Mekruksavanich Anuchit Jitpattanakul Image Information and Intelligence Laboratory Department of Computer EngineeringFaculty of EngineeringMahidol UniversityNakhon Pathom73170Thailand Department of Computer Engineering School of Information and Communication TechnologyUniversity of PhayaoPhayaoThailand Department of Mathematics Faculty of Applied ScienceKing Mongkut’s University of Technology North BangkokBangkok 10800Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Science and Technology Research Institute King Mongkut’s University of Technology North BangkokBangkok 10800Thailand

Falls are the contributing factor to both fatal and nonfatal injuries in the ***,pre-impact fall detection,which identifies a fall before the body collides with the floor,would be ***,researchers have turned their attention from post-impact fall detection to pre-impact fall ***-impact fall detection solutions typically use either a threshold-based or machine learning-based approach,although the threshold value would be difficult to accu-rately determine in threshold-based ***,while additional features could sometimes assist in categorizing falls and non-falls more precisely,the esti-mated determination of the significant features would be too time-intensive,thus using a significant portion of the algorithm’s operating *** this work,we developed a deep residual network with aggregation transformation called FDSNeXt for a pre-impact fall detection approach employing wearable inertial *** proposed network was introduced to address the limitations of fea-ture extraction,threshold definition,and algorithm *** training on a large-scale motion dataset,the KFall dataset,and straightforward evaluation with standard metrics,the proposed approach identified pre-impact and impact falls with high accuracy of 91.87 and 92.52%,*** addition,we have inves-tigated fall detection’s performances of three state-of-the-art deep learning models such as a convolutional neural network(CNN),a long short-term memory neural network(LSTM),and a hybrid model(CNN-LSTM).The experimental results showed that the proposed FDSNeXt model outperformed these deep learning models(CNN,LSTM,and CNN-LSTM)with significant improvements.

关键词： Pre-impact fall detection deep learning wearable sensor deep residual network

来源：评论

学校读者我要写书评

暂无评论

Privacy-Preserving Human Activity Recognition in Smart Homes using Deep Learning and Edge Computing for Real-Time Processing 5

Privacy-Preserving Human Activity Recognition in Smart Homes...

引用

5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Mariappan, K. Gopal, Baskar Parthasarathy, J. Sreekanth, G.R. Nanmaran, R. Jegajothi, B. SRM Institute of Science and Technology Department of Computer Science and Applications Chennai India College of Engineering University of Technology & Applied Science Nizwa Oman Computer Science & Engineering Jayalakshmi Institute Of Technology Dharmapuri India Information Technology Nandha Engineering College Erode52 India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India SRS Tech Solutions Chennai India

ISBN: (纸本)9798331530013

The rapid advancement of smart home technologies necessitates efficient human activity recognition (HAR) systems while ensuring user privacy. This research presents a novel architecture that integrates deep learning and edge computing for real-time HAR, emphasizing privacy protection. By employing differential privacy methods and federated learning, the approach minimizes personal data exposure risks. Using the UCI HAR dataset, the model achieves an accuracy of 96.50%, with precision, recall, and Fl-score metrics of 95.75%, 96.20%, and 95.97%, respectively. The results indicate high accuracy and adaptability to varying smart home environments, while edge computing ensures minimal latency. This work highlights the importance of balancing performance and privacy in HAR systems, paving the way for secure smart home applications. Additionally, it points to the potential of deep learning in medical diagnostics, suggesting future research could enhance diagnostic capabilities across various medical imaging modalities, including CT scans and MRIs, to improve healthcare outcomes. © 2024 IEEE.

关键词： Smart homes

来源：评论

学校读者我要写书评

暂无评论

Co-Occurrence Histogram Based Ensemble of Classifiers for Classification of Cervical Cancer Cells

引用

Journal of Electronic science and technology 2022年第3期20卷 270-281页

作者： Rajesh Yakkundimath Varsha Jadhav Basavaraj Anami Naveen Malvade the Department of Computer Science and Engineering Karnataka Lingayat Education Institute of TechnologyHubballi580027India Department of Information Science and Engineering Shri Dharmasthala Manjunatheshwara College of Engineering and TechnologyDharwad580008India the Department of Information Science and Engineering Smt.Kamala&Sri.Venkappa M.Agadi College of Engineering and TechnologyLakshmeshwar582116India the Visvesvaraya Technological University Belagavi590018India

To explore the potential of conventional image processing techniques in the classification of cervical cancer cells, in this work, a co-occurrence histogram method was employed for image feature extraction and an ensemble classifier was developed by combining the base classifiers, namely, the artificial neural network(ANN),random forest(RF), and support vector machine(SVM), for image classification. The segmented pap-smear cell image dataset was constructed by the k-means clustering technique and used to evaluate the performance of the ensemble classifier which was formed by the combination of above considered base classifiers. The result was also compared with that achieved by the individual base classifiers as well as that trained with color, texture, and shape features. The maximum average classification accuracy of 93.44% was obtained when the ensemble classifier was applied and trained with co-occurrence histogram features, which indicates that the ensemble classifier trained with co-occurrence histogram features is more suitable and advantageous for the classification of cervical cancer cells.

关键词： Cervical cancer co-occurrence histogram ensemble classification k-means clustering

来源：评论

学校读者我要写书评

暂无评论

Decentralized Continuous Group Key Agreement for UAV Ad-Hoc Network 6th

Decentralized Continuous Group Key Agreement for UAV Ad-Hoc...

引用

6th International Conference on Machine Learning for Cyber Security, ML4CS 2024

作者： Hong, Shijia Zhou, Tianqi Yang, Huijie Shen, Mingdi Zheng, Wenying School of Information Science and Engineering Zhejiang Sci-Tech University Hangzhou310018 China School of Computer Science and Technology Zhejiang Sci-Tech University Hangzhou310018 China

ISBN: (纸本)9789819645657

With the rise of the Industrial Internet of Things (IIoT), Unmanned Aerial Vehicles (UAVs) have emerged as the preferred sensing tools for data collection. However, resource-constrained UAVs often face challenges in handling complex group key agreement processes, while key management in the flying ad hoc network (FANET) is also susceptible to single points of failure. To address this issue, this paper proposes a blockchain-based continuous group key agreement protocol for FANET. The blockchain is collectively maintained by UAVs and serves to record dynamic changes within the FANET. Additionally, it employs a ratchet tree to integrate a pseudo-random function generator with an updatable public key encryption scheme, effectively mitigating the risk of single points of failure, reducing communication overhead, and enhancing security and self-healing capabilities. Simulation experiments demonstrate that this protocol outperforms existing key agreement schemes in terms of reduced delay and improved security. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

Exploring New Opportunities for Service Delivery within Next Generation Networks 15

Exploring New Opportunities for Service Delivery within Next...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Manjunath, C. Naval, Preeti Upadhyay, Ramanand School of Engineering and Technology Department of Mechanical Engineering Karnataka Bangalore India Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India Sanskrit University Department of Computer Science Engineering Uttar Pradesh Mathura India

ISBN: (纸本)9798350370249

These days, generation is advancing in the contemporary international at an unheard-of fee with ever-increasing bandwidth usage on contemporary networks, which might be fragile and not able to cope with related increases. This ends in demanding situations in sustaining the potential for assembly of subsequent-era desires. Therefore, research and development for the net of factors (IoT), the Semantic internet, wi-fi Sensor Networks (WSNs), and side Computing, amongst other emerging fields, are especially relevant today. To address those concerns, studies on revolutionary solutions to improve provider shipping for the subsequent technology of networks are being explored. One viable answer is the adoption of software-defined networking (SDN) and virtualized community technologies and offerings for Time-touchy Networking (TSN). TSN integrates numerous IT and IoT devices and services in addition to the deployment of specialized network technology, which includes 5G cell networks. This lets in for the switch of facts without compromising the overall performance and reliability of services, which includes actual-Time Networking (RTN). Furthermore, quantum technologies for IoT, consisting of Quantum Computing (qc) and Quantum Networks (QN), may be explored to enhance the scalability and reliability of provider shipping networks. © 2024 IEEE.

关键词： Quantum computers

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：