检索结果-内蒙古大学图书馆

A predictive surrogate model based on linear and nonlinear solution manifold reduction in cardiovascular FSI: A comparative study

引用

computers in Biology and Medicine 2025年 189卷 109959-109959页

作者： Barzegar Gerdroodbary, M. Salavatidezfouli, Sajad Department of Electromechanical Engineering C-MAST-Center for Mechanical and Aerospace Science and Technology Universidade da Beira Interior Covilha Portugal Department of Electrical and Computer Engineering Aarhus University Aarhus Denmark

This study investigates the fluid-structure interaction (FSI) simulation of the abdominal aorta, with a particular focus on the hemodynamic alterations induced by aneurysmal deformations. The hemodynamic behavior within the aorta is highly dependent on the geometric characteristics of the aneurysm, necessitating the use of patient-specific models to ensure accurate predictions. The primary objective of this research is to enhance the predictive capability of flow and structural indices in a complex FSI biomechanical setting under varying physiological conditions, namely rest and exercise states. This paper presents a comparative analysis between two distinct yet promising surrogate models: Proper Orthogonal Decomposition coupled with Long Short-Term Memory (POD + LSTM) and Convolutional Neural Network combined with Long Short-Term Memory (CNN + LSTM). The methodology, model selection, and comparative performance analysis are discussed in detail, providing insights into the efficacy and limitations of each approach in the context of personalized cardiovascular simulations. © 2025 The Authors

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

PokkahScan - An Intelligent Drone for detection of Pokkah Boeng Disease in Sugarcane Using Transfer Learning and eXplainable AI 15

PokkahScan - An Intelligent Drone for detection of Pokkah Bo...

引用

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

作者： Ravikumar, Rahul Sujith Kumaar, M.V. Krishna, Shirish Jayasurkanth, M. Yasshwanth, V.R. Saravanan, Sanjay Kishore Thangavel, Senthil Kumar Thangeswari, S. Somasundaram, K. Shanmugam, Selvanayaki Kolandapalayam Amrita School of Artificial Intelligence Amrita Vishwa Vidyapeetham Center for Computational Engineering & Networking Coimbatore India Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Aerospace Engineering Coimbatore India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India Tamilnadu Agricultural University Department of Plant Pathology Coimbatore India Amrita School of Physical Sciences Department of Mathematics Coimbatore India Ashland University Department of Mathematics and Computer Science OH United States

ISBN: (纸本)9798350370249

Traditional systems for crop disease detection often rely on manual inspection methods, which are time-consuming, labor-intensive, and prone to human error. In this context, the necessity of using transfer learning becomes apparent as it allows for leveraging pre-existing knowledge from large datasets to improve the performance of models trained on smaller, domain-specific datasets. IoT has also played a significant role in our automation process. Transfer learning leveraging MobilenetV2 is particularly beneficial for this problem of detecting Pokkah Boeng disease in sugarcane crops, as it enables the adaptation of pretrained models, such as MobileNetV2, to accurately identify Pokkah Boeng disease even with limited set of data. LIME is employed to provide transparent explanations for the model's predictions, enhancing its interpretability and facilitating informed decision-making in agricultural management. By combining transfer learning with xAI and aerial drone technology, having add-ons of IoT and facilitating a comprehensive comparative study, PokkahScan offers a comprehensive solution for timely disease detection and crop management in sugarcane farming. © 2024 IEEE.

关键词： Agricultural Analytics Crop Management Disease Detection Early Detection IoT MobilenetV2 Pokkah Boeng Disease Sugarcane Crop Transfer Learning xAI

来源：评论

学校读者我要写书评

暂无评论

Robust Reversible Audio Watermarking Scheme for Telemedicine and Privacy Protection

引用

computers, Materials & Continua 2022年第5期71卷 3035-3050页

作者： Xiaorui Zhang Xun Sun Xingming Sun Wei Sun Sunil Kumar Jha Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science&TechnologyNanjing210044China IT Fundamentals and Education Technologies Applications University of Information Technology and Management in RzeszowRzeszow Voivodeship100031Poland

The leakage of medical audio data in telemedicine seriously violates the privacy of *** order to avoid the leakage of patient information in telemedicine,a two-stage reversible robust audio watermarking algorithm is proposed to protect medical audio *** scheme decomposes the medical audio into two independent embedding domains,embeds the robust watermark and the reversible watermark into the two domains *** order to ensure the audio quality,the Hurst exponent is used to find a suitable position for watermark *** to the independence of the two embedding domains,the embedding of the second-stage reversible watermark will not affect the first-stage watermark,so the robustness of the first-stage watermark can be well *** the second stage,the correlation between the sampling points in the medical audio is used to modify the hidden bits of the histogram to reduce the modification of the medical audio and reduce the distortion caused by reversible *** experiments show that this scheme has strong robustness against signal processing operations such as MP3 compression of 48 db,additive white Gaussian noise(AWGN)of 20 db,low-pass filtering,resampling,re-quantization and other attacks,and has good imperceptibility.

关键词： Telemedicine privacy protection audio watermarking robust reversible watermarking two-stage embedding

来源：评论

学校读者我要写书评

暂无评论

A Robust 3-D Medical Watermarking Based on Wavelet Transform for Data Protection

引用

computer Systems science & engineering 2022年第6期41卷 1043-1056页

作者： Xiaorui Zhang Wenfang Zhang Wei Sun Xingming Sun Sunil Kumar Jha Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China IT Fundamentals and Education Technologies Applications University of Information Technology and Management in RzeszowRzeszowVoivodeship100031Poland

In a telemedicine diagnosis system,the emergence of 3D imaging enables doctors to make clearer judgments,and its accuracy also directly affects doctors’diagnosis of the *** order to ensure the safe transmission and storage of medical data,a 3D medical watermarking algorithm based on wavelet transform is proposed in this *** proposed algorithm employs the principal component analysis(PCA)transform to reduce the data dimension,which can minimize the error between the extracted components and the original data in the mean square ***,this algorithm helps to create a bacterial foraging model based on particle swarm optimization(BF-PSO),by which the optimal wavelet coefficient is found for embedding and is used as the absolute feature of watermark embedding,thereby achieving the optimal balance between embedding capacity and imperceptibility.A series of experimental results from MATLAB software based on the standard MRI brain volume dataset demonstrate that the proposed algorithm has strong robustness and make the 3D model have small deformation after embedding the watermark.

关键词： 3-D medical watermarking robust watermarking PCA BF-PSO

来源：评论

学校读者我要写书评

暂无评论

Exploiting Partial Observability and Optimized Simple State Representations in Deep Q-Learning 20

Exploiting Partial Observability and Optimized Simple State ...

引用

20th International Conference on Frontiers of Information Technology, FIT 2023

作者： Mahmood, Danyaal Arshad, Usama Ali, Raja Hashim Ul Abideen, Zain Shah, Muhammad Huzaifa Khan, Talha Ali Ijaz, Ali Zeeshan Ali, Nisar Siddique, Abu Bakar Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany Applications & Nanostructure BCMaterials Basque Center for Materials Spain University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350395785

The advent of deep Q-learning has opened up new possibilities in training autonomous agents to perform intelligently in intricate settings. This research work examines the potential of deep Q-learning in the paradigmatic Snake game, which requires an agent to navigate a grid, ingest food items, and avoid collisions. We incorporated a partially observable game state, introducing a novel level of difficulty to the task. Our proposed approach utilizes a straightforward feed-forward neural network to estimate Q-values of potential actions, given the current state. The agent progressively learns to balance between exploration and exploitation, allowing it to navigate optimally in the game scenario. Most existing studies in deep Q-learning for the Snake game overlook the complexities posed by partial observability, focusing mainly on fully observable state representations. Furthermore, the majority of the investigations delve into the efficacy of complex neural network architectures, leaving a gap in the understanding of the performance of more simplistic feed-forward networks. Our study fills this gap by offering an in-depth exploration and analysis of deep Q-learning strategies, especially when applied to partially observable game states and simple neural networks. Simulations and results reveal that adopting simpler state representations can significantly boost the agent's performance in the Snake game. A more detailed and intricate reward function is also observed to enhance the agent's decision-making skills and overall proficiency in the game. These observations underscore the significance of optimizing state representations and reward functions for maximizing the performance of deep Q-learning agents in the Snake game. © 2023 IEEE.

关键词： Autonomous agents

来源：评论

学校读者我要写书评

暂无评论

Investigating novel machine learning based intrusion detection models for NSL-KDD data sets

Investigating novel machine learning based intrusion detecti...

引用

2023 International Conference on IT and Industrial Technologies, ICIT 2023

作者： Shah, Muhammad Huzaifa Abu Bakar, Muhammad Ali, Raja Hashim Ul Abideen, Zain Arshad, Usama Ijaz, Ali Zeeshan Ali, Nisar Imad, Muhammad Nabi, Said Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Spain University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350315974

This study investigates the application of the Mutual Information (MI) feature selection technique to improve the accuracy of Machine Learning (ML) models on NSL-KDD datasets, building upon prior research. Six ML models, namely Decision Tree (DT), Logistic Regression (LR), K-Nearest Neighbor (KNN), Random Forest (RF), Naive Bayes (NB), and Support Vector Machine (SVM) with different kernels (1st, 2nd, and 3rd), are implemented for classification purposes. The proposed DT model in this study shows higher accuracy than the DT model proposed in the original paper by Ingre et al. for Intrusion Detection System (IDS). Additionally, a multi-class classification model for NSL-KDD datasets is developed, considering both normalized and non-normalized features. Interestingly, it is observed that the models trained without normalized features achieve higher accuracies compared to those trained with normalized features. Moreover, the study enhances the classification performance of the DT-based IDS using the Correlation based Feature Selection (CFS) technique for feature selection. The proposed IDS is evaluated both before and after feature selection for multi-class classification (normal and various attack types) and binary classification (normal and abnormal data). © 2023 IEEE.

关键词： Feature Selection

来源：评论

学校读者我要写书评

暂无评论

Efficient Exploitation for Off-Policy Reinforcement Learning via a Novel Multi-scale Tabular Buffer 3

Efficient Exploitation for Off-Policy Reinforcement Learning...

引用

3rd Asia Conference on Information engineering, ACIE 2023

作者： Guo, Shicheng Xue, Wei Zhao, Wei Shen, Yuanxia Yu, Gaohang Anhui University of Technology School of Computer Science and Technology Maanshan243032 China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei230088 China Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Maanshan243032 China Hangzhou Dianzi University School of Sciences Hangzhou310018 China

ISBN: (纸本)9798350333121

This paper is concerned with sample efficiency in off-policy reinforcement learning. For efficient exploitation of samples, we propose a novel method for updating buffer that allows important samples to remain in the buffer longer. Specifically, we first propose a new metric to determine the importance of a sample, which takes into account the value of the state and the period confidence level. Secondly, based on this metric, the multi-scale tabular buffer method is proposed, which sets multiple tables in the original buffer, and the size of the tables changes with time. Experiments show that the proposed method outperforms some state-of-the-art methods on a range of benchmark tasks. In particular, on some tasks, our method only requires half of samples to achieve the state-of-the-art performance. © 2023 IEEE.

关键词： Efficiency

来源：评论

学校读者我要写书评

暂无评论

An Online Scheduling Approach for Cloud Computing on Mobile 4

An Online Scheduling Approach for Cloud Computing on Mobile

引用

4th International Conference on Frontiers Technology of Information and computer, ICFTIC 2022

作者： Tao, Tao Li, Hao Zhang, SiHan Xu, RuiLing Huang, Jun Zheng, Xiao School of Computer Science and Technology Anhui University of Technology Ma'anshan China Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Ma'anshan China

ISBN: (纸本)9798350321951

In the mobile cloud computing (MCC) environment, the distribution of user computing activities often suffers from excessive round-trip latency and low processing efficiency. Much of the present research is focused on MCC system-wide task assignment issues, with minimal focus on personalized computational task scheduling optimization. As a result, we present a new dynamic assignment approach for online user computing tasks in our work, which turns the dynamic task scheduling issue into a contextual multi-armed bandit (CMAB) problem. So we use the Thompson algorithm to perceive current contextual information. In terms of computing efficiency and latency performance, simulation results show that the suggested strategy surpasses other contextual and context-free benchmarkers. © 2022 IEEE.

关键词： Mobile cloud computing

来源：评论

学校读者我要写书评

暂无评论

Identifying COVID-19 through X-ray and CT scan images using Machine Learning 18

Identifying COVID-19 through X-ray and CT scan images using ...

引用

18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Shehzadi, Maham Arshad, Usama Abideen, Zainul Ali, Raja Hashim Khan, Ammad Aslam Ijaz, Ali Zeeshan BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany Virtual University of Pakistan Dept. of Biological Sciences Rawalpindi Pakistan National University of Computer & Emerging Sciences Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350328172

In the current global scenario, marked by the COVID-19 pandemic, it has become imperative for healthcare systems around the globe to swiftly and accurately diagnose the disease. This is where cutting-edge approaches such as machine learning (ML) come into play, specifically, ML-based identification of COVID-19 from chest X-ray and CT-Scan imagery. Our research collective is devotedly working towards devising a robust, accurate ML model capable of deciphering these images to identify COVID-19 effectively. This involves the application of a variety of AI algorithms and vast data resources. Our work is grounded on a large, annotated image dataset that aids in training and refining the model. Our findings indicate that ML methodologies can discern COVID-19 from chest X-ray and CT scan images with a mean accuracy of 90%. Such a tool holds promise in aiding medical practitioners in prompt and precise patient diagnosis, thereby improving patient prognosis. The most notable contribution of our study lies in the creation of an ML model that can accurately detect COVID-19 in chest X-ray and CT-Scan images, potentially aiding in early disease detection and management. © 2023 IEEE.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

Neural P3M: A Long-Range Interaction Modeling Enhancer for Geometric GNNs 38

Neural P3M: A Long-Range Interaction Modeling Enhancer for G...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Wang, Yusong Cheng, Chaoran Li, Shaoning Ren, Yuxuan Shao, Bin Liu, Ge Heng, Pheng-Ann Zheng, Nanning National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University China University of Illinois Urbana-Champaign United States Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong University of Science and Technology of China China Microsoft Research AI4Science United States

Geometric graph neural networks (GNNs) have emerged as powerful tools for modeling molecular geometry. However, they encounter limitations in effectively capturing long-range interactions in large molecular systems due to the localization assumption of GNN. To address this challenge, we introduce Neural P3M, a versatile enhancer of geometric GNNs to expand the scope of their capabilities by incorporating mesh points alongside atoms and reimaging traditional mathematical operations in a trainable manner. Neural P3M exhibits flexibility across a wide range of molecular systems and demonstrates remarkable accuracy in predicting energies and forces, outperforming on benchmarks such as the MD22 dataset. It also achieves an average improvement of 22% on the OE62 dataset while integrating with various architectures. Codes are available at https://***/OnlyLoveKFC/Neural_P3M. © 2024 Neural information processing systems foundation. All rights reserved.

关键词：

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：