检索结果-内蒙古大学图书馆

2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Durai, R. S. Vishnu Vijayakumar, R. Lakshmisridevi, S. Bhanu, Shaik Thasleem Arunkumar, U. Sri Ramakrishna Engineering College Department of Computer Science and Engineering Tamilnadu Coimbatore641022 India B.S. Abdur Rahman Crescent Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai600048 India Rajalakshmi Engineering College Department of Electronics and Communication Engineering Tamilnadu Chennai602105 India Department of Electrical and Electronics Engineering Tamil Nadu Coimbatore641021 India

ISBN: (纸本)9798350383348

Precision agriculture is a cutting-edge farming strategy that maximizes harvests by using cutting-edge technology and data-driven decision-making. Optical sensors and other Internet of Things (IoT) devices have great promise to revolutionize farming operations in this setting. Sensor networks and Machine Learning (ML) based tracking devices are in great demand because of the precise data extraction and analysis they give. This research was undertaken with the goal of reducing agricultural hazards and promoting smart farming practices. Diseases caused by insects and other diseases may reduce crop yields if not addressed quickly. Thus, in this study, we provide a unique artificial swarm fish optimized naive bayes (ASFONB) method for keeping an eye on the health of the soil and preventing diseases from manifesting in cotton plants' leaves. In this research, numerous important indicators of crop growth and health were monitored using Internet of Things (IoT) devices equipped with optical sensors. The environmental factors like as temperature, humidity, light intensity, and chlorophyll content are recorded by these sensors. The proposed method involves sending the collected data to a central server for processing and analysis via wireless transmission. Once the disease has been detected, the information will be sent to the farmers via Android app. The Android app can show the chemical concentration in a container with soil factors like humidity, temperature, and wetness. Using an Android app, you may control the relay and hence the power supply and chemical sprinkler system. The experimental findings demonstrate that the proposed solution outperforms the status quo in disease identification. © 2024 IEEE.

关键词： Agriculture IoT Optimization Approach

来源：评论

学校读者我要写书评

暂无评论

Reversible Data Hiding in Shared Images with Separate Cover Image Reconstruction and Secret Extraction

引用

IEEE Transactions on Cloud Computing 2024年第1期12卷 186-199页

作者： Xiong, Lizhi Han, Xiao Yang, Ching-Nung Shi, Yun-Qing Nanjing University of Information Science and Technology School of Computer Science Nanjing210044 China Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology Engineering Research Center of Digital Forensics Ministry of Education Nanjing210044 China Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai200240 China National Dong Hwa University Department of Computer Science & Information Engineering Hualien974301 Taiwan New Jersey Institute of Technology ECE Department NewarkNJ07102 United States

Reversible data hiding is widely utilized for secure communication and copyright protection. Recently, to improve embedding capacity and visual quality of stego-images, some Partial Reversible Data Hiding (PRDH) schemes are proposed. But these schemes are over the plaintext domain. To protect the privacy of the cover image, Reversible Data Hiding in Encrypted Images (RDHEI) techniques are preferred. In addition, the full separability of cover image reconstruction and data restoration is also an important characteristic that cannot be achieved by most RDHEI schemes. To solve the issues, a partial and a complete Reversible Data Hiding in Shared Images with Separate Cover Image Reconstruction and Secret Extraction (RDHSI-SRE) are proposed in this paper. In the proposed schemes, the secret data is divided by Secret Sharing (SS). Then, the marked shared images are generated based on the proposed modify-and-recalculate strategy. The receiver can extract embedded data and reconstruct the image separably using k-out-of-n marked shared images. In the embedding phase of partial RDHSI-SRE (PRDHSI-SRE), the pixel values are modified according to the proposed Minimizing-Square-Errors Strategy to achieve high visual quality, and the complete RDHSI-SRE (CRDHSI-SRE) embeds data by modifying random coefficients to achieve reversibility. The experimental results and theoretical analyses demonstrate that the proposed schemes have a high embedding performance. Most importantly, the proposed schemes are fault-tolerant and completely separable. © 2013 IEEE.

关键词： Image reconstruction

来源：评论

学校读者我要写书评

暂无评论

Mean-field chaos diffusion models 24

Mean-field chaos diffusion models

引用

Proceedings of the 41st International Conference on Machine Learning

作者： Sungwoo Park Dongjun Kim Ahmed M. Alaa Department of Electrical Engineering and Computer Sciences UC Berkeley Department of Computer Science Stanford Department of Electrical Engineering and Computer Sciences UC Berkeley and UCSF

In this paper, we introduce a new class of score-based generative models (SGMs) designed to handle high-cardinality data distributions by leveraging concepts from mean-field theory. We present mean-field chaos diffusion models (MF-CDMs), which address the curse of dimensionality inherent in high-cardinality data by utilizing the propagation of chaos property of interacting particles. By treating high-cardinality data as a large stochastic system of interacting particles, we develop a novel score-matching method for infinitedimensional chaotic particle systems and propose an approximation scheme that employs a subdivision strategy for efficient training. Our theoretical and empirical results demonstrate the scalability and effectiveness of MF-CDMs for managing large high-cardinality data structures, such as 3D point clouds.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Retinal Blood Vessel Tracking and Diameter Estimation via Gaussian Process With Rider Optimization Algorithm

引用

IEEE Journal of Biomedical and Health Informatics 2024年第3期28卷 1173-1184页

作者： Ahmad, Nehal Lai, Kuan-Ting Tanveer, M. Indian Institute of Technology Indore Simrol Department of Mathematics Indore453552 India National Taipei University of Technology Department of International Program of Electrical Engineering and Computer Science Taipei106 Taiwan National Taipei University of Technology Department of Electronic Engineering Taipei106 Taiwan

Retinal blood vessels structure analysis is an important step in the detection of ocular diseases such as diabetic retinopathy and retinopathy of prematurity. Accurate tracking and estimation of retinal blood vessels in terms of their diameter remains a major challenge in retinal structure analysis. In this research, we develop a rider-based Gaussian approach for accurate tracking and diameter estimation of retinal blood vessels. The diameter and curvature of the blood vessel are assumed as the Gaussian processes. The features are determined for training the Gaussian process using Radon transform. The kernel hyperparameter of Gaussian processes is optimized using Rider Optimization Algorithm for evaluating the direction of the vessel. Multiple Gaussian processes are used for detecting the bifurcations and the difference in the prediction direction is quantified. The performance of the proposed Rider-based Gaussian process is evaluated with mean and standard deviation. Our method achieved high performance with the standard deviation of 0.2499 and mean average of 0.0147, which outperformed the state-of-the-art method by 6.32%. Although the proposed model outperformed the state-of-the-art method in normal blood vessels, in future research, one can include tortuous blood vessels of different retinopathy patients, which would be more challenging due to large angle variations. We used Rider-based Gaussian process for tracking blood vessels to obtain the diameter of retinal blood vessels, and the method performed well on the 'STrutred Analysis of the REtina (STARE) Database' accessed on Oct. 2020 (https://***/~ahoover/stare/). To the best of our knowledge, this experiment is one of the most recent analysis using this type of algorithm. © 2013 IEEE.

关键词： Blood vessels

来源：评论

学校读者我要写书评

暂无评论

Dataset Ratio Influence on kNN Classification Results 33rd

Dataset Ratio Influence on kNN Classification Results

引用

33rd International Conference on Organization and Technology of Maintenance, OTO 2024

作者： Gaće, Marin Galba, Tomislav Baumgartner, Alfonzo Livada, Časlav Faculty of Electrical Engineering Josip Juraj Strossmayer University of Osijek Computer Science and Information Technology Osijek Kneza Trpimira 2B Osijek31000 Croatia

ISBN: (纸本)9783031805967

Classification is used in many fields today, and for most of them machine learning algorithms can be used to make a decision. This article investigates the effects of different sizes of training and test datasets on the accuracy of classification using both classical k-nearest neighbors (kNN) and weighted k-nearest neighbors (wkNN) methods. This study systematically evaluates how different ratios of training to test data affect classification performance and provides a comprehensive analysis of the interplay between the ratios of the datasets and the parameter k. The results show that the ratio of training to test datasets exerts a more significant influence on classification accuracy than the choice of the parameter k. In particular, extreme ratios, where the training dataset is either much larger or much smaller than the test dataset, lead to suboptimal classification results. This emphasizes the importance of carefully selecting an appropriate balance between training and test data to ensure robust and reliable classification results. By highlighting the critical role of the ratio between datasets, this paper provides valuable insights for optimizing kNN and weighted kNN methods in practical applications. The results have far-reaching implications for the design and implementation of machine learning models and emphasize the need for a balanced data distribution to achieve high classification accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： k-nearest neighbors

来源：评论

学校读者我要写书评

暂无评论

Smart Processing of Exceptional Values in electrical Networks Using Neuro-Fuzzy Algorithms

Smart Processing of Exceptional Values in Electrical Network...

引用

2024 IEEE International Students' Conference on electrical, Electronics and computer science, SCEECS 2024

作者： Anwar, Mahammad Kothiyal, Arnav Nandankar, Praful V. Barve, Amit Patil, Harshal Maranan, Ramya Ballari Institute of Technology and Management Ballari Visvesvaraya Technological University Department of Electrical and Electronics Engineering Karnataka India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Government College of Nagpur Department of Electrical Engineering Maharashtra 441108 India Parul Institute of Technology Faculty of Engineering and Technology Parul University Department of Computer Science and Engineering P.O. Limda Gujarat 391760 India Department of Computer Science and Engineering Maharashtra Pune412115 India Saveetha School of Engineering Simats Department of Research and Innovation Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350348460

This research introduces a novel smart meter topology within the context of an integrated PV-Wind hybrid system for DC electric micro-networks. The proposed topology incorporates advanced techniques such as Linear Interpolation, Artificial Neural Network (ANN), and Adaptive Neuro-Fuzzy Inference System (ANFIS) for identifying and correcting outliers in the collected data. The integration of the smart meter with outlier detection and correction modules enhances the accuracy and reliability of the measurements, contributing to the overall efficiency of the micro-network. Linear Interpolation is employed to estimate and fill in missing data points, ensuring a continuous and consistent dataset. The ANN module is designed to identify outliers by learning patterns from historical data, allowing for real-time anomaly detection. Furthermore, the ANFIS module enhances the system's adaptability by providing a fuzzy logicbased approach to correct outliers, taking into account the dynamic nature of renewable energy sources in the hybrid system. The proposed smart meter topology offers a comprehensive solution for ensuring the integrity of data collected in DC electric micronetworks. By leveraging the synergies of Linear Interpolation, ANN, and ANFIS, the system demonstrates improved robustness against data anomalies, thereby enabling more accurate monitoring, control, and optimization of the PV-Wind hybrid system. This research contributes to the advancement of smart grid technologies, particularly in the realm of renewable energy micro-networks, by addressing the challenges associated with data quality and reliability. © 2024 IEEE.

关键词： Integrated Control Mean Squared Error Micro-Networks Modeling outliers smart meter

来源：评论

学校读者我要写书评

暂无评论

Empowering Healthcare with AI: Brain Tumor Detection Using MRI and Multiple Algorithms

Empowering Healthcare with AI: Brain Tumor Detection Using M...

引用

2024 International Conference on science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Arowolo, Micheal Olaolu Oluwatobiloba Ajayi, Williams Olawoye, Prisca Babajide, Oluwatomilola Aigbogun, Happiness Eric Damola Salawu, Mukhtar Adebiyi, Marion Olubunmi Adebiyi, Ayodele University of Missouri Department of Electrical Engineering and Computer Science Columbia65201 United States Landmark University Department of Computer Science Omu-Aran Nigeria Western Illinois University Department of Public Health 61455 United States Stowers Institute for Medical Research Kansas CityMO United States Kwara State University Department of Computer Science Malete Nigeria

ISBN: (纸本)9798350358155

Artificial intelligence (AI) has revolutionized neuroimaging, especially in the identification of brain cancers, by giving patients greater control and providing highly accurate diagnostic imaging. utilizing MRI data, this study investigates the feasibility of AI-driven brain tumor diagnosis utilizing methods including K-nearest neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), and Naive Bayes (NB). Careful data preprocessing allowed us to access Kaggle's brain MRI datasets labeled with cancer information, which allowed us to obtain accurate results. While Naive Bayes achieved 60% accuracy and 84% sensitivity, SVM achieved 82% sensitivity and 62% accuracy. Enhanced sensitivity and precision, in addition to 63% accuracy, were shared by Decision Tree and KNN. These findings suggest that AI could be helpful in the multi-method detection strategy that is essential for the correct diagnosis of brain tumors. This study provides clinicians with flexible diagnostic tools powered by artificial intelligence, opening a new chapter in the fight against neurological disorders and for better patient outcomes. The combination of artificial intelligence with magnetic resonance imaging (MRI) holds great promise for the better early detection and treatment planning of brain cancers. As AI keeps changing the healthcare landscape, these studies show how AI can revolutionize diagnostics and the fight against neurological illnesses. © 2024 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

In-Memory Template Matching with Approximated PCC Computation Leveraging Memristive System

In-Memory Template Matching with Approximated PCC Computatio...

引用

IEEE International Symposium on Quality Electronic Design

作者： Sree Nirmillo Biswash Tushar Sk Hasibul Alam Graham Buchanan Rocco Febbo Hritom Das Garrett S. Rose Department of Electrical Engineering and Computer Science The University of Tennessee Knoxville TN USA Department of Electrical and Computer Engineering Oklahoma State University

ISBN: (数字)9798331509422

ISBN: (纸本)9798331509439

Template matching is a well-known computer vision algorithm that involves scanning a template across various parts of an image. The template is correlated within this algorithm using a similarity or matching score, such as the Pearson correlation coefficient (PCC). A chieving a m ore a ccurate match necessitates searching many regions using the PCC metric, which is hindered by the Von Neumann Bottleneck, resulting in increased energy consumption and delays. Therefore, this paper proposes an energy-efficient, c omprehensive memristive in-memory computing architecture for template matching with its physical design, where the PCC computation unit sensor readout unit, DAC, demultiplexers, in-memory memristive computing array, ADC, running sum module, fixed point operation unit and comparator. The PCC equation is approximated, considering the limitations of the hardware characteristics and application requirements. The proposed approximated memristive in-memory based template-matching scheme demonstrates competitive performance compared to the Von Neumann system and achieves around 678× improvement in the power-delay product. Lastly, a threshold-based optimization strategy is suggested to reduce energy consumption in the application.

关键词： Correlation coefficient Energy consumption Voltage In-memory computing Hardware Energy efficiency Delays Computational efficiency Physical design Sensor arrays

来源：评论

学校读者我要写书评

暂无评论

A WSN Node Fault Diagnosis Model Based on BRB with Self-Adaptive Quality Factor

引用

computers, Materials & Continua 2023年第4期75卷 1157-1177页

作者： Guo-Wen Sun Gang Xiang Wei He Kai Tang Zi-Yi Wang Hai-Long Zhu School of Computer Science and Information Engineering Harbin Normal UniversitySongbei DistrictHarbin150025China Beihang University School of Automation Science and Electrical Engineering Beijing100191China Beijing Aerospace Automatic Control Institute Beijing100074China Rocket Force University of Engineering Xi’anShaanxi Province710025China

Wireless sensor networks (WSNs) operate in complex and harshenvironments;thus, node faults are inevitable. Therefore, fault diagnosis ofthe WSNs node is essential. Affected by the harsh working environment ofWSNs and wireless data transmission, the data collected by WSNs containnoisy data, leading to unreliable data among the data features extracted duringfault diagnosis. To reduce the influence of unreliable data features on faultdiagnosis accuracy, this paper proposes a belief rule base (BRB) with a selfadaptivequality factor (BRB-SAQF) fault diagnosis model. First, the datafeatures required for WSN node fault diagnosis are extracted. Second, thequality factors of input attributes are introduced and calculated. Third, themodel inference process with an attribute quality factor is designed. Fourth,the projection covariance matrix adaptation evolution strategy (P-CMA-ES)algorithm is used to optimize the model’s initial parameters. Finally, the effectivenessof the proposed model is verified by comparing the commonly usedfault diagnosis methods for WSN nodes with the BRB method consideringstatic attribute reliability (BRB-Sr). The experimental results show that BRBSAQFcan reduce the influence of unreliable data features. The self-adaptivequality factor calculation method is more reasonable and accurate than thestatic attribute reliability method.

关键词： Self-adaptive quality factor belief rule base wireless sensor networks fault diagnosis

来源：评论

学校读者我要写书评

暂无评论

Adaptive Video Streaming: An AI-Driven Approach Leveraging Cloud and Edge Computing

Adaptive Video Streaming: An AI-Driven Approach Leveraging C...

引用

2023 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2023

作者： Darwich, Mahmoud Khalil, Kasem Ismail, Yasser Bayoumi, Magdy University of Mount Union Department of Mathematics and Computer Science AllianceOH44601 United States University of Mississippi University Department of Electrical and Computer Engineering MS38677 United States Southern University and AandM College Electrical Engineering Department Baton RougeLA70807 United States University of Louisiana at Lafayette Department of Electrical and Computer Engineering LA70504 United States

ISBN: (纸本)9798350322347

Video streaming efficiency remains a major challenge, with increasing demands for high-resolution content and minimal buffering times. We propose a novel solution to enhance user experiences. Our approach combines adaptive streaming, AI algorithms predicting network conditions, and edge computing to optimize task allocation. These technologies aim to reduce latency, lower network traffic, and facilitate seamless transitions in video quality. Our results improve the streaming services and enable real-time video applications in remote or mobile environments, presenting substantial implications for the future of video streaming. © 2023 IEEE.

关键词： Video streaming

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：