检索结果-内蒙古大学图书馆

1st international conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Divya, S. Saranya, S. Mahendra Engineering College Department of Computer Science and Engineering Namakkal India

ISBN: (纸本)9798331529246

The massive progression of the multi-tier edge cloud computing systems integration has created the possibility of data orchestration and resource control in edge and cloud layers. However, there are many shortcomings, including inappropriate methods of scheduling tasks and security breaches that prevent scalability. To solve these problems, we designed an energy-efficient and security-aware task offloading framework for multi-tier edge cloud computing. Firstly, we estimate the energy ratio of tasks using a Fuzzy Logic Energy Management (FL-EM) algorithm and distribute resources according to different layers. Subsequently, the task-offloading parallel squirrel (TO-PS) method is used to effectively extend the task with better energy consumption and computational efficiency. Finally, it is decided to protect the work data using the Provable Energy-Aware Cryptographic Security (PE-ACS) algorithm. The proposed algorithm achieves a 96.7% success rate in protecting high-level data from cybercrime. Additionally, it enhances security and task performance while reducing energy consumption in multi-tier edge cloud computing systems. © 2025 ieee.

关键词： Cloud security

来源：评论

学校读者我要写书评

暂无评论

An Ensemble-Based Framework for Enhanced Missing data Imputation 14

An Ensemble-Based Framework for Enhanced Missing Data Imputa...

引用

14th ieee international conference on Communication systems and Network Technologies, CSNT 2025

作者： Alam, Samiya Dubey, Aditya Bisen, Dhananjay Department of Electronics Engineering Madhav Institute of Technology & Science M.P. Gwalior India Centre for Internet of Things Madhav Institute of Technology & Science M.P. Gwalior India

ISBN: (纸本)9798331531935

Missing data is a pervasive issue in many real-world datasets, often leading to biased estimates, reduced statistical power, and invalid conclusions if not properly addressed. Traditional imputation methods, such as mean, median, and K-nearest neighbors (KNN), each have their advantages but often fail to provide consistently accurate estimates across different datasets and missing data patterns. Advanced methods, including IterativeImputer and matrix factorization-based SoftImpute, offer improvements but still face limitations. In this paper, we propose a novel ensemble-based imputation method that synergistically combines the strengths of KNN, IterativeImputer, mean, median, and SoftImpute. The ensemble method leverages the performance of each individual technique to provide the most accurate imputation for each missing value. We conducted extensive evaluations on a dataset with artificially introduced 2%, 5%, 10% and 20% missing data. Our results, analysed using root mean square error (RMSE), demonstrate that the proposed ensemble method consistently outperforms each individual imputation method across various levels of missing data. This research highlights the effectiveness of an ensemble approach in enhancing data quality and reliability, offering a robust solution for practitioners dealing with incomplete datasets. © 2025 ieee.

关键词： data reduction

来源：评论

学校读者我要写书评

暂无评论

Assisted Analysis of Big data Algorithms for Public Security in Crime Governance

Assisted Analysis of Big Data Algorithms for Public Security...

引用

2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Wang, Yiran Shanghai University of Political Science and Law Shanghai201500 China

ISBN: (纸本)9798331509828

With the high-quality development of society, public safety is an important control direction, and the use of computers to assist in intelligent crime warning is an important way. This study combines the multi-modal information of text data streams, video data and audio data from the system level to build an intelligent crime warning model algorithm. The proposed algorithm can improve the accuracy of the crime monitoring and enhance the effect of smart management and control. In the text data stream intelligent analysis module, this study designed an abnormal data stream detection algorithm based on function signature matching. This algorithm can effectively identify potential security threats in the network through complex data streams. In the video data module, this study proposes an improved YOLOv11 target detection algorithm. The algorithm uses GIOU Loss to optimize the target box regression loss, which enhances the detection ability of small targets to a certain extent, thereby realizing real-time warning and analysis of abnormal human behavior. In the audio data analysis module, this study combines den-oising algorithms and spectrum analysis techniques to improve the accuracy of audio abnormal signal detection. The designed experiment was conducted on a real public safety data-set and analyzed through separate tests of data stream, video, and audio. The results showed that the proposed method has a stable warning accuracy under multi-modal information fusion. © 2025 ieee.

关键词： data accuracy

来源：评论

学校读者我要写书评

暂无评论

CNN-Based Machine Learning Model for Disease Prediction in Crops

CNN-Based Machine Learning Model for Disease Prediction in C...

引用

2025 ieee international conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Choudhary, Vijay Thakur, Archana Bhalse, Nisha IPS Academy Institute of Engineering & Science Department of Computer Science & Engineering Indore India School of Computer Science & IT Devi Ahilya University Indore India

ISBN: (纸本)9798331512965

Potato crops are vital to global food security, but they are susceptible to several diseases that hinder growth and yield. Traditional methods of detecting these diseases rely on labor-intensive lab tests and human observation. This research presents an enhanced machine learning model for detecting and categorizing potato leaf diseases, specifically early blight and late blight, using Convolutional Neural Networks (CNN). The model is trained on various datasets of potato leaf images, utilizing techniques such as hyper-parameter tuning, data augmentation, and transfer learning. The proposed model attains an accuracy of 96.11%, significantly improving upon existing methods. Image processing techniques, Segmentation is performed using K-means technique and feature extraction is done by utilizing gray-level co-occurrence matrix, along with multi-class support vector machines for classification, are employed. Additionally, the study shows that CNN performs better than Artificial Neural Networks (ANN) in image classification. The study demonstrates how AI and deep learning technology can be used in agriculture to improve crop management and enable early disease prediction. Experiments have been conducted using the real image dataset. © 2025 ieee.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Edge-Computing Enabled Si Photonics Multimodal Sensor with Integrated Photonic Convolutional Processor 38

Edge-Computing Enabled Si Photonics Multimodal Sensor with I...

引用

38th ieee international conference on Micro Electro Mechanical systems, MEMS 2025

作者： Xiao, Zian Ren, Zhihao Zhuge, Yangyang Zhang, Zixuan Zhou, Jingkai Xu, Siyu Xu, Cheng Dong, Bowei Lee, Chengkuo Agency for Science Technology and Research Singapore National University of Singapore Department of Electrical & Computer Engineering Singapore Singapore National Centre for Advanced Integrated Photonics S2-B3-01 50 Nanyang Avenue Singapore

ISBN: (纸本)9798331508890

Photonic integrated circuits provide compact and efficient solutions for multimodal spectroscopic sensors. However, the resulting sensory data is highly complex and contains significant redundancy. To circumvent high communication cost for redundancy, the photonic sensor and processor are brought into intimacy and propose a photonic multimodal edge computing system. A microring resonator crossbar is used as photonic processor with 5-bit accuracy. The in-situ processing of sensory data is demonstrated in this system, achieving a classification accuracy of 94.27% across 45 different classes. The multimodal edge computing system demonstrates the feasibility of integrating photonic processors and photonic sensors to enhance the photonic data processing capability. © 2025 ieee.

关键词： In situ processing

来源：评论

学校读者我要写书评

暂无评论

Analysis of Machine Learning Models for Anomaly Detection Using PMU data

Analysis of Machine Learning Models for Anomaly Detection Us...

引用

2025 ieee Texas Power and Energy conference, TPEC 2025

作者： Dasari, Bharath Kumar Shrestha, Rajendra Tadese, Yared Brhane Souto, Larissa Chamana, Manohar Mohammadpour, Mostafa Adeyanju, Olatunji Bayne, Stephen Wind Science and Engineering Texas Tech University LubbockTX United States Electrical and Computer Engineering Texas Tech University LubbockTX United States National Wind Institute Texas Tech University LubbockTX United States

ISBN: (纸本)9798331541125

The growing complexity of modern power systems, coupled with the integration of Phasor Measurement Units (PMUs), necessitates robust and efficient anomaly detection mechanisms to ensure grid reliability and security. This study evaluates various machine learning models, including traditional methods (Logistic Regression, SVC), ensemble methods (Random Forest, AdaBoost, CatBoost, LightGBM), and deep learning architectures (CNN, LSTM, CNN-LSTM), for multi-class anomaly detection using PMU data. A dataset, generated from ieee 39-bus test systems, simulates realistic scenarios with physical and cyber anomalies augmented by Gaussian and Laplacian noise to reflect real-world conditions. The models were assessed based on metrics such as F1 score, precision, recall, accuracy, and computational efficiency. Results show that ensemble methods, particularly CatBoost and LightGBM, achieved the best balance of classification accuracy and efficiency, making them ideal for real-time applications, while deep learning models like CNN-LSTM excelled at capturing complex patterns but were computationally intensive. Traditional models offered interpretability but struggled with higher-dimensional data. This research underscores the trade-offs between performance and efficiency across methods and highlights the potential of ensemble models as scalable solutions for grid monitoring. © 2025 ieee.

关键词： Phasor measurement units

来源：评论

学校读者我要写书评

暂无评论

Maintaining Privacy in Contact Tracing systems for Pandemics Through Edge Computing 1

Maintaining Privacy in Contact Tracing Systems for Pandemics...

引用

1st international conference on AIML-Applications for Engineering and Technology, ICAET 2025

作者： Tejaswini, Virupakshi Sirisala, NageswaraRao K.S.R.M. College of Engineering Department of Computer Science and Engineering A.P Kadapa India

ISBN: (纸本)9798350355611

data processing and publication are crucial today with the advancements in technologies such as edge computing and IoT(Internet of Things). A vast volume of data is being generated by internet-connected devices, often on a continuous basis. The COVID-19 pandemic data exemplifies this trend of constant data flow, as the global medical emergency has necessitated ongoing data collection. This data must be processed and analyzed, and subsequently published, while ensuring personal privacy is upheld. Edge computing devices can handle various tasks like data preprocessing and analysis in any setting. It is crucial to process data before uploading it to the cloud and sharing it with advisories or media outlets, as improper handling could lead to privacy breaches. To protect personal privacy, measures such as differential privacy techniques are implemented to protect data. Edge computing technologies offer several advantages, including efficient data processing close to the origin to minimize internet data transmission capacity use and enhanced security for confidential information on public cloud infrastructures. © 2025 ieee.

关键词： data Sharing Differential Privacy (DP) Edge Computing Individual Privacy

来源：评论

学校读者我要写书评

暂无评论

Channel Map-Based Angle Domain Multiple Access for Cell-Free Massive MIMO Communications

引用

ieee Journal on Selected Topics in Signal Processing 2025年第2期19卷 366-380页

作者： Chen, Shuaifei Wang, Cheng-Xiang Li, Junling Huang, Chen Chang, Hengtai Huang, Yusong Huang, Jie Chen, Yunfei Purple Mountain Laboratories Nanjing211111 China Southeast University National Mobile Communications Research Laboratory School of Information Science and Engineering Nanjing211189 China Purple Mountain Laboratories Pervasive Communication Research Center Nanjing211111 China Shandong University School of Information Science and Engineering Qingdao266237 China University of Durham Department of Engineering DurhamDH1 3LE United Kingdom

Being aware of the channel and its properties is critical for coherent transmission in massive multiple-input multiple-output (M-MIMO) systems due to the large channel dimension in the space domain. In cell-free (CF) systems, the channel dimension increases further as each user is served by multiple access points, with a significant burden on signal processing. Angle domain transmission and channel maps promise to alleviate this burden by reducing channel dimensions in the angle domain and providing a priori channel information through channel measurements and modeling, respectively. In this paper, we propose a channel map-based angle domain multiple access scheme for the uplink CF M-MIMO communications. First, we propose an angle domain data reception scheme constituting receive combining and large-scale fading decoding to maximize spectral efficiency. Then, we derive an initial access criterion utilizing the angle domain channel similarity between users, based on which we propose pilot assignment and access point selection schemes for better trade-offs between spectral and energy efficiency. Finally, we construct two channel map-based transmission mechanisms by wielding different levels of channel information, where a tailored data reception scheme with a newly derived spectral efficiency upper bound is also proposed for quantitative evaluation. Simulation results show that the proposed channel map-based angle domain schemes outperform their space domain alternatives and the schemes without using channel maps regarding spectral and energy efficiency. © 2025 ieee.

关键词： Spectrum efficiency

来源：评论

学校读者我要写书评

暂无评论

Particle-based data-driven Nonlinear State Estimation of Model-free Process from Nonlinear Measurements

Particle-based Data-driven Nonlinear State Estimation of Mod...

引用

2025 ieee international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Ghosh, Anubhab Eldar, Yonina C. Chatterjee, Saikat Digital Futures Centre School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Faculty of Mathematics and Computer Science The Weizmann Institute of Science Israel

ISBN: (纸本)9798350368741

We consider the problem of causal filtering of a model-free process from (noisy) nonlinear measurements. The 'model-free process' means that we do not have a state-space model (SSM) of the process dynamics, limiting the use of traditional model-driven filters, such as unscented Kalman filter (UKF) and particle filter (PF). To address the problem we propose a particle-based data-driven nonlinear state estimation (pDANSE) method. In pDANSE, a recurrent neural network (RNN) provides the statistical parameters of a Gaussian prior of the underlying state, and particles are then drawn from the prior to compute the posterior moments. pDANSE is typically trained in a semi-supervised fashion. For our experiments we study the use of half-wave rectification as a nonlinear transformation of measurements. We first show that an unsupervised learning-based method under-performs, and subsequently the semi-supervised learning-based pDANSE performs satisfactorily. Using Lorenz-63 system as benchmark, pDANSE is found to be competitive against a model-driven PF that knows the exact SSM. © 2025 ieee.

关键词： deep neural network Kalman filter particle filter semi-supervised learning State estimation

来源：评论

学校读者我要写书评

暂无评论

Intelligent Algorithm System for Security Governance Driven by Big data in Autonomous systems 2

Intelligent Algorithm System for Security Governance Driven ...

引用

2nd international conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Wang, Yiran Shanghai University of Political Science and Law Shanghai201500 China

ISBN: (纸本)9798331505745

With the advancement of big data encryption and decryption technology, the role of data in national governance and social governance has become increasingly prominent. In order to effectively deal with the increasingly complex security governance issues, this study proposes the novel big data-driven intelligent algorithm system. The purpose of this study is to optimize the challenges of the network attack identification, protection measures, and encryption processing of information leakage in security governance through real-time monitoring and predictive analysis of data. The core of the designed system includes two modules: network attack detection algorithm and information leakage modeling encryption algorithm. From a methodological perspective, this study proposes a model for predicting abnormal network traffic based on chaos dynamics. This groundbreaking model uses time series analysis to identify and locate potential DDoS attack traffic. At the same time, combined with information entropy theory, a cascade network attack chain analysis model for DDoS attack protection is designed. At the data security level, an encryption algorithm based on chaos theory is used to effectively protect against information leakage. Through experimental verification, it is known that the proposed encryption algorithm can effectively retain the original data during the information encryption and decryption process. At the same time, through encryption algorithm verification, the security and anti-attack ability of information are improved. © 2025 ieee.

关键词： Information leakage

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：