检索结果-内蒙古大学图书馆

International Conference on Advanced Computing and Communication systems (ICACCS)

作者： Shelly Chelsy Pally Jenefa J Dept. of Computer Science and Engineering CHRIST (Deemed to be University) engaluru Karnataka India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

Rice holds a significant position in India, especially in the southern part of the country, where people tend to eat some rice at least once a day. Farmers are facing a huge loss due to diseases in leaf, which is the main problem of agriculture. By using techniques like machine learning, main problems detection can be done. This review, discusses common plant diseases that affect the leaf. Some include Leaf Spots, Rusts, Fusarium Wilt, Early Blight, Powdery Mildew and Downey Mildew. Our research found that machine learning techniques on rice plants make finding diseases on leaves easier. Finally, we concluded that the most accurate method is the Enhanced VGG16, with an accuracy of 99.60% because it is really good at spotting diseases on rice leaves because it's great at recognizing the small details and patterns in leaf pictures. This helps it to tell the diseases apart more accurately and make fewer mistakes in identifying them.

关键词： Productivity Accuracy Reviews Prevention and mitigation Computational modeling Crops Prediction algorithms Medical diagnosis Reliability Diseases

来源：评论

学校读者我要写书评

暂无评论

Modular Deep Reinforcement Learning: Enhancing Flexibility through Agent Splitting

Modular Deep Reinforcement Learning: Enhancing Flexibility t...

引用

IEEE International Conference on Big Data

作者： Chong Tian Danda B. Rawat Dept. of Electrical Engineering & Computer Science Howard University Washington DC USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Deep reinforcement learning (DRL) has shown remarkable success in tackling complex tasks by learning representations directly from raw data. However, as DRL agents become increasingly sophisticated, they often face challenges in adapting to new tasks or environments due to their monolithic structure. In this paper, we prove that by decomposing the agent into modular components, we enable more efficient adaptation as well as more efficient than that of the singular DRL agent on tackling complex tasks. Overall, our work contributes to advancing the field of DRL by introducing a flexible and scalable framework for agent decomposition. By enabling the creation of modular agents, we empower DRL systems to efficiently adapt to changing environments, learn new tasks with minimal intervention, and achieve higher levels of performance and versatility.

关键词： Big Data Deep reinforcement learning Faces

来源：评论

学校读者我要写书评

暂无评论

A New Holistic Approach for Prosthesis Polymeric Aortic Valve Design

A New Holistic Approach for Prosthesis Polymeric Aortic Valv...

引用

Thermal, Mechanical and Multiphysics Simulation and Experiments in Micro-Electronics and Micro-systems EuroSimE

作者： Müge Yaren Yaşartürk Emirhan Yığıt Reza Daryanı Emre Cenk Ersan Mustafa Serdar Çelebı Dept. of Computational Science and Engineering Istanbul Technical University Istanbul Turkey Dept. of Computer Engineering Istanbul Beykent University Istanbul Turkey Dept. of Mechanical Engineering Istanbul Technical University Istanbul Turkiye Dept. of Computational Science and Engineering Istanbul Technical University Istanbul Turkiye

ISBN: (数字)9798350393002

ISBN: (纸本)9798350393019

Aortic valve insufficiency is a life-threatening condition. The primary treatment approach is the replacement of the native valve with prosthetic valves in severe cases. However, current prostheses often lead to complications such as durability issues, the need for lifelong anticoagulation medicine usage, and tissue rejection. As a result, multiple surgical interventions are frequently required for prosthesis replacement. This study highlights the importance of a comprehensive prosthetic design process by encompassing patient-specific design, careful material selection, and advanced three-dimensional fabrication, which is essential for addressing these challenges. To enhance the mechanical durability of polymeric heart valve prostheses, we introduce a framework that combines fluid-structure interaction (FSI) simulations to obtain hemodynamic properties prior to advanced structural analyses. While our methodology is based on determining hemodynamic loads with FSI simulations and transferring them to an additional finite element model (FEM) that captures the detailed mechanical properties of valve tissue, the results presented in this study are obtained using more traditional loading conditions in FEM to ensure the validity and comparability of our findings. These results serve as a foundation for future studies incorporating full FSI-driven structural simulations. These analyses lead to the creation of a geometrically optimized design, fabricated with a digital light processing-based (DLP) method. Subsequently, mechanical testing was conducted to evaluate the effects of this manufacturing technique on material behavior, producing more realistic material model coefficients for use in numerical simulations. Consequently, different ratios of material rigidity in fiber-reinforced models are investigated with FEM analysis. An important novelty of this approach is to be able to detect stress accumulations by taking hemodynamic indices into account to optimize the stress di

关键词： Analytical models Valves Finite element analysis Hemodynamics Numerical models Polymers Heart valves Stress Prosthetics Load modeling

来源：评论

学校读者我要写书评

暂无评论

Unsupervised Insider Threat Detection Using Multi-Head Self-Attention Mechanisms

Unsupervised Insider Threat Detection Using Multi-Head Self-...

引用

Intelligent Cybersecurity Conference (ICSC)

作者： Simon Bertrand Pascal Germain Nadia Tawbi dept. of Computer Science & Software Engineering Laval University Québec Canada

ISBN: (数字)9798350354775

ISBN: (纸本)9798350354782

We propose an unsupervised insider threat detection system that learns normal user behaviors through audit data using neural networks equipped with multi-head self-attention mechanisms. The attention mechanisms learn precise normal user behaviors using large event windows. The key idea is to consider a sequence abnormal when it exhibits some events that are not likely to happen given the preceding events sequence. In addition, our method provides insights into detected threats through an event-based scoring system to facilitate threat understanding by security experts. Furthermore, the proposed solution does not require intensive audit logs pre-processing, such as manual domain knowledge-rich features extraction and data balancing. Thus, the proposed approach is easy to implement and use across organizations regardless of their expertise level. The proposed solution also provides the ability to detect threats in real-time. On the benchmark dataset CERT version 4.2, our solution, combined with hyperparameter search by Bayesian optimization, outperforms the previous state-of-the-art approaches with an area under the ROC curve of 97.23%, a recall of 91.51%, an accuracy of 93.13% and a false positive rate of 6.8%.

关键词： Neural networks Manuals Feature extraction Vectors Threat assessment Data models Real-time systems Bayes methods Optimization Tuning

来源：评论

学校读者我要写书评

暂无评论

Compressive Recovery of Signals Defined on Perturbed Graphs

arXiv

引用

arXiv 2024年

作者： Ghosh, Sabyasachi Rajwade, Ajit Dept. of Computer Science and Engineering Indian Institute of Technology Bombay Mumbai India

Recovery of signals with elements defined on the nodes of a graph, from compressive measurements is an important problem, which can arise in various domains such as sensor networks, image reconstruction and group testing. In some scenarios, the graph may not be accurately known, and there may exist a few edge additions or deletions relative to a ground truth graph. Such perturbations, even if small in number, significantly affect the Graph Fourier Transform (GFT). This impedes recovery of signals which may have sparse representations in the GFT bases of the ground truth graph. We present an algorithm which simultaneously recovers the signal from the compressive measurements and also corrects the graph perturbations. We analyze some important theoretical properties of the algorithm. Our approach to correction for graph perturbations is based on model selection techniques such as cross-validation in compressed sensing. We validate our algorithm on signals which have a sparse representation in the GFT bases of many commonly used graphs in the network science literature. An application to compressive image reconstruction is also presented, where graph perturbations are modeled as undesirable graph edges linking pixels with significant intensity difference. In all experiments, our algorithm clearly outperforms baseline techniques which either ignore the perturbations or use first order approximations to the perturbations in the GFT bases. Copyright © 2024, The Authors. All rights reserved.

关键词： Recovery

来源：评论

学校读者我要写书评

暂无评论

Color correction by QAB (Quantum particle swarm optimization-ant colony optimization- back propagation) method 16

Color correction by QAB (Quantum particle swarm optimization...

引用

16th IEEE International Conference on computer science and Education, ICCSE 2021

作者： Sumaiya Armanuzzaman, M.D. KUET Dept. of Computer Science and Engineering Khulna Bangladesh Univarsity at Buffalo Dept. of Computer Science and Engineering NY United States

ISBN: (纸本)9781665414685

Now-a-days color correction has been so well accepted method for recovering color information. We proposed a color correction technique based on QPSO-ACO-BP algorithm. Firstly, the QPSO and ACO algorithms are merged. Then the optimized values are retrieved through the merged QPSO-ACO algorithm. BP model is utilized for accomplishing a model regarding this. This model performed with Macbeth Color Checker on the images for color correction. The experimental results show that our proposed technique goes well for color correction. We checked some quality metrics like PSNR (Peak Signal to Noise Ratio) and MSE (Mean Square Error) for the evaluation of results and found the desired improvement. © 2021 IEEE.

关键词： Color

来源：评论

学校读者我要写书评

暂无评论

Explainable Light-Weight Deep Learning Pipeline for Improved Drought Stress Identification

arXiv

引用

arXiv 2024年

作者： Patra, Aswini Kumar Sahoo, Lingaraj Dept. of Computer Science and Engineering North Eastern Regional Institute of Science and Technology Itanagar India Dept. of Bio-Science and Bio-Engineering Indian Institute of Technology Guwahati India

Early identification of drought stress in crops is vital for implementing effective mitigation measures and reducing yield loss. Non-invasive imaging techniques hold immense potential by capturing subtle physiological changes in plants under water deficit. Sensor based imaging data serves as a rich source of information for machine learning and deep learning algorithms, facilitating further analysis aimed at identifying drought stress. While these approaches yield favorable results, real-time field applications requires algorithms specifically designed for the complexities of natural agricultural conditions. Our work proposes a novel deep learning framework for classifying drought stress in potato crops captured by UAVs in natural settings. The novelty lies in the synergistic combination of a pre-trained network with carefully designed custom layers. This architecture leverages the pre-trained network’s feature extraction capabilities while the custom layers enable targeted dimensionality reduction and enhanced regularization, ultimately leading to improved performance. A key innovation of our work involves the integration of Gradient-Class Activation Mapping (Grad-CAM), an explainability technique. Grad-CAM sheds light on the internal workings of the deep learning model, typically referred to as a"black box." By visualizing the model’s focus areas within the images, Grad-CAM fosters interpretability and builds trust in the model’s decision-making process. Our proposed framework achieves superior performance, particularly with the DenseNet121 pre-trained network, reaching a precision of 97% to identify the stressed class with an overall accuracy of 91%. Comparative analysis of existing state-of-the-art object detection algorithms reveals the superiority of our approach in significantly higher precision and accuracy. Thus, our explainable deep learning framework offers powerful approach for drought stress identification with high accuracy and actionable insights. Cop

关键词： Cams

来源：评论

学校读者我要写书评

暂无评论

A Comparative Analysis of Early Departure Buttons in Coordinated Control of EV Charging

A Comparative Analysis of Early Departure Buttons in Coordin...

引用

IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Baver Ozceylan Gerwin Hoogsteen Johann L. Hurink Dept. of Electrical Engineering Mathematics and Computer Science University of Twente Enschede The Netherlands

ISBN: (数字)9798350390421

ISBN: (纸本)9798350390438

The increasing adoption of electric vehicles (EVs) necessitates the efficient management of large EV parking facilities to prevent them from exceeding grid capacity and to improve the overall user experience. This paper introduces a data-driven approach for coordinated control of EV charging in an office parking facility, integrating Early Departure Buttons (EDB) into the system. These buttons provide a binary option for users to indicate an earlier departure than a predefined time. We employ the EDB data to improve departure time estimations and to address issues where EVs receive no or only very low energy. We utilize three datasets originating from different geographical locations. One dataset displays a user pattern where users leave shortly after completing their charging, unlike the other datasets which follow typical working hours. Our simulations show that the unique user pattern significantly increases fairness among users, and integrating EDBs improves fairness for the other datasets to levels similar to those of quick station turnover.

关键词： Measurement Schedules Europe Estimation Charging stations Electric vehicle charging User experience Smart grids

来源：评论

学校读者我要写书评

暂无评论

Comprehensive Forecasting-Based Analysis of Hybrid and Stacked Stateful/Stateless Models

arXiv

引用

arXiv 2024年

作者： Saha, Swayamjit Dept. of Computer Science and Engineering Mississippi State University Starkville United States

Wind speed is a powerful source of renewable energy, which can be used as an alternative to the nonrenewable resources for production of electricity. Renewable sources are clean, infinite and do not impact the environment negatively during production of electrical energy. However, while eliciting electrical energy from renewable resources viz. solar irradiance, wind speed, hydro should require special planning failing which may result in huge loss of labour and money for setting up the system. In this paper, we discuss four deep recurrent neural networks viz. Stacked Stateless LSTM, Stacked Stateless GRU, Stacked Stateful LSTM and Statcked Stateful GRU which will be used to predict wind speed on a short-term basis for the airport sites beside two campuses of Mississippi State University. The paper does a comprehensive analysis of the performance of the models used describing their architectures and how efficiently they elicit the results with the help of RMSE values. A detailed description of the time and space complexities of the above models has also been discussed. © 2024, CC BY.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Explainable Adversarial Attacks on Coarse-to-Fine Classifiers

Explainable Adversarial Attacks on Coarse-to-Fine Classifier...

引用

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

作者： Akram Heidarizadeh Connor Hatfield Lorenzo Lazzarotto HanQin Cai George Atia Dept. of Electrical and Computer Engineering University of Central Florida Orlando FL USA School of Technology Pontifícia Universidade Católica do Rio Grande do Sul Porto Alegre Brazil Dept. of Statistics and Data Science Dept. of Computer Science University of Central Florida Orlando FL USA Dept. of Electrical and Computer Engineering Dept. of Computer Science University of Central Florida Orlando FL USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Traditional adversarial attacks typically aim to alter the predicted labels of input images by generating perturbations that are imperceptible to the human eye. However, these approaches often lack explainability. Moreover, most existing work on adversarial attacks focuses on single-stage classifiers, but multi-stage classifiers are largely unexplored. In this paper, we introduce instance-based adversarial attacks for multi-stage classifiers, leveraging Layer-wise Relevance Propagation (LRP), which assigns relevance scores to pixels based on their influence on classification outcomes. Our approach generates explainable adversarial perturbations by utilizing LRP to identify and target key features critical for both coarse and fine-grained classifications. Unlike conventional attacks, our method not only induces misclassification but also enhances the interpretability of the model’s behavior across classification stages, as demonstrated by experimental results.

关键词： Perturbation methods Decision making Signal processing Acoustics Speech processing

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：